SEPIA User Manual and Developer`s Guide
Contents
1. These variables are initialized in the initializeNewRound method of the privacy peer class only shown in parts init counters initialSharesToReceive numberOflnputPeers finalResultsToDo numberOflnputPeers finalResults null akon ONARAN on COANDOAARWNM ore NOOR ODM 22 4 TUTORIAL CREATE YOUR OWN PROTOCOL 4 2 Read Input Data Generate Send and Receive Shares Step 4 Read Input Data Besides the configuration data your protocol certainly needs some input data Therefore you should implement the readDataFromFile method in the input peers class to read the input data from a file For our tutorial protocol we just add some code to generate the data instead of reading it from a file inputData new long numberOfltems for int inputIndex 0 inputlIndex lt numberOfltems inputIndex inputData inputIndex inputIndex return true Step 5 Generate and Send Shares The input peers have to generate Shamir shares of their inputs For that we add one method to generate and one to retrieve the initial shares in the TutorialPeer class public synchronized void generatelnitialShares if initialSharesGenerated initialSharesGenerated true initialShares mpcShamirSharing generateShares inputData protected long getlinitialSharesForPrivacyPeer int privacyPeerlndex return initialShares privacyPeerIndex In the Tutorial2. mark operationtype Chooses the basic MPC operation to be benchmarked multiply equal lessthan Or smallintervaltest default multiply lowerbound Lower bound used for the smallintervaltest operation default 1 upperbound Upper bound used for the smallintervaltest operation default 10 Table 13 Specific properties Benchmark Protocol The resulting filter is then written into a file The format is the same as for the set union protocol Section 2 3 7 The configuration options of this protocol can be seen in Tables 11 property iscounting is not supported and 12 2 3 11 Benchmark Protocol The benchmark protocol is used to assess the performance of parallel MPC operations such as multiplications equality and less than comparisons It does not have input and output files but input data is generated randomly Statistics for each round are written to the log file It measures the number of parallel operations per second and the data volume sent over the network Table 13 describes the supported configuration options The number of operations performed is given by the property mpc numberofitemsintimeslot 2 4 Controlling Privacy of a Computation In the above sections we described the parameters peers minpeers for setting a minimum number of input peers and mpc minpeers for setting a minimum number of privacy peers These parameters can be used to control the privacy of an ongoing computation When a new prot
3. numberoftimeslots Number of rounds timeslots or windows to process numberof itemsin Specifies the size of the fixed length input data vectors For instance for com timeslot plete port histograms this is 65 536 activeprivacypeers Sequence of all privacy peer addresses in the form lt adr1 gt lt adr2 gt lt adrN gt Each address has the form lt ID gt lt host gt lt port gt Table 1 Basic parameters for input and privacy peers peers Key connection Description keystore Name of the Java KeyStore JKS where the private key and trusted certificates of other peers are stored used for SSL connections For details on setting up the KeyStore read Section 2 5 keystorepassword Password for opening the KeyStore keystorealias Alias of this peer s private key in the KeyStore keypassword Password for using this peer s private key usecompression Enables automatic compression of data e g the shares sent over connections true false default false This makes sense if the field size is much smaller than the datatype holding the shares Shares are stored and transmitted in long variables 64 bits If the field size is 20 bits compression saves roughly 2 3 of the data volume Table 2 Connection parameters for input and privacy peers connection participating in the addition protocol This section describes the general properties for all input and privacy peer
4. can be tedious especially if various configurations with different parameters need to be evaluated To facilitate the creation of configurations we implemented the SEPIA configuration editor Table 14 gives an overview of the files involved 3 1 Quickstart To get started right away just run winStartGUI bat on a Windows OS startGUI sh on a Linux Unix OS There are two modes for the configuration editor one mode is command line only and the second is the graphical user interface The Syntax is as follows java jar MakeConfig jar glc some properties 16 3 SEPIA CONFIGURATION EDITOR Filename Description config properties IPhosts txt PPhosts txt MakeContfig jar sampleKeytoollnput txt testConfigKeyStore jks startGui sh winStartGUI bat sepia_logo png Main configuration file Change settings here if you use the tool from command line List of all possible input peer hosts List of all possible privacy peer hosts Java archive of this Tool Input file needed if a full PKI is generated Default keystore all peers use the same private public key Shell script to start the GUI version of the tool Script to start the GUI version on Windows Logo needed for the about dialog sepia jar yourprotocolhere jar Sepia Library must be named exactly this way The jar file of your custom protocol can have any name Table 14 Files required by the configuration editor The parameter g s
5. a File 25 won public long getFinalResult return finalResults The sendFinalResult method is called from the run method class TutorialProtocolPrivacyPeer ToPeer as soon as all results are ready After the final results were sent to all input peers the final result is reported to the observers and the method checks if a new round should be started or if all protocols should be stopped Step 9 Receive Final Results Of course the input peers need some code to handle the re ception of the final results After receiving a protocol message the protocol thread notifies the observers which in this case is the TutorialPeer class Therefore message handling is imple mented in the notificationReceived method comments and logging ommitted OANDARWN if object instanceof TutorialMessage TutorialMessage tutorialMessage TutorialMessage object if tutorialMessage isDummyMessage tutorialMessage setisFinalResultMessage true if tutorialMessage isFinalResultMessage finalResultsToDo if finalResults null amp amp tutorialMessage getResults null finalResults tutorialMessage getResults if finalResultsToDo lt 0 VectorData dummy new VectorData FinalResultEvent finalResultEvent new FinalResultEvent this myAlphalndex getMyPeerlD tutorialMessage getSenderlD dummy finalResultEvent setVerificationSuccessful true sendNotification finalR
6. is used as the event key and the second is used as the weight count of the event Irrespective of the format the s events with biggest weights are used as input of the corresponding input peer Output files contain one reconstructed event per line with the following format lt event key gt lt aggregated weight gt lt peer count gt lt list of peers reporting the event gt 2 3 5 Top K Protocol The top k protocol described in detail in 1 privately and probabilistically computes the top k items An item e is defined by a key weight tuple e k w The input peers create hash tables of a fixed size h For each item they store the key and weight at the position given by the hash of the key If local collisions occur the node only reports key and value of the item with the bigger value They then share these hash tables The PPs aggregate the items of each slot of the hash tables with the same key In case of different items being in the same slot a subalgorithm computes the key with the largest weight for that slot 10 2 RUNNING SEPIA PROTOCOLS Key mpc weighted Description setintersection maxweight Defines the maximum acceptable event weight in input verification default 256 maxfeature Defines the maximum value that is used for event keys default 1401085391 If this is big enough to represent the maximum weight sum it is used as the field size featurecount The number of event
7. msg TutorialMessage object if msg isDummyMessage msg setisInitialSharesMessage true if msg isInitialSharesMessage 4The update method of Java s Observer design pattern is defined in the TutorialBase class and calls this method whenever it receives a TutorialMessage 10 11 12 13 14 15 16 17 18 19 OANDARWNM SCOMONODOARWND ni 4 3 Computing Functions on Secrets 23 TutorialPeerInfo peerlnfo getPeerlnfoByPeerlD msg getSenderID peerinfo setinitialShares msg getInitialShares initialSharesToReceive if initialSharesToReceive lt 0 startNextPPProtocolStep else create exception see source code for details 4 3 Computing Functions on Secrets Step 7 Compute Functions At this point we get to the most interesting part computing a function on the shared secrets The code for the computation goes into the TutorialPrivacyPeer class Here is the code for computing the product of all the input peers data items public void startProductComputations int activelnputPeers connectionManager getNumberOfConnectedPeers false true initializeNewOperationSet numberOfltems operationIDs new int numberOfltems long data null for int operationIndex 0 operationIndex lt numberOfltems operationIndex operation IDs operationIndex operationIndex data new long activelnputPeers int datalndex 0 for in
8. uniquely identifiable object such as popular URLs of which the peers want to compute the total number of hits The weight w reflects the impact count of this event object e g the frequency of the event in the current time window or a classification on a severity scale 2Note that in the code the protocol is called weighted set intersection protocol and event keys are called features This is also reflected in configuration properties 2 3 Protocol Specific Usage and Configuration 9 Key Description mpc entropy Sets the Tsallis exponent q used in default 2 Currently SEPIA supports tsallisexponent integer values greater than 1 Table 7 Specific properties Entropy Protocol Input Peer1 23 0 0 0 5 Input Peer2 0 0 0 7 0 Input Peer3 0 0 0 75 12 Intermediate 1 0 0 1 1 Output 3 Table 8 Example round of the distinct count protocol Each peer shares at most s local events per time window The goal of the protocol is to reconstruct an event if and only if a minimum number of input peers T report the same event and the aggre gated weight is at least T The rationale behind this definition is that an input peer does not want to reconstruct local events that are unique in the set of all input peers exposing sensitive information asymmetrically But if he knew that for example three other input peers report the same event e g a specific intrusion alert he would be willing to con
9. 1 Properties used for Set Operation Protocols 2 3 6 Set Intersection Protocol SEPIA includes a suite of private set operations protocols based on bloom filters In these protocols the sets are represented by bloom filters A bloom filter for representing a set S 21 22 2 of r elements is an array BF of s bits initially set to 0 The bloom filter uses k independent hash functions hy h with range 1 s For each element x S the bits at positions h x are set to 1 for 1 lt i lt k Counting Bloom Filters CBF are a generalization of bloom filters which use integer arrays instead of bit arrays enabling representation of multisets For more details please see 3 The set intersection protocol takes as input a file describing a set of items with one set element per line From the input set the IPs compute their bloom filter representation of their set The filters are then sent to the privacy peers The privacy peers then compute the bloom filter representing the intersection set position wise That is for all positions u with 1 lt u lt s they perform the logical AND BFy u BFi u A BFo u A A BFn u 2 For the multiset case the equation is as follows BF u min min min BFi u BF2 u BF3 u BFn u 3 All positions of BF are then reconstructed and sent back to the input peers The input peers then check for each item of their input set if it is in the intersection set Each
10. 2 for more details Table 6 shows an example with concrete input and output values In this protocol the output is a single value Hy The specific configuration properties for the entropy protocol are listed in Table 7 Since the entropy protocol is built on top of the addition protocol also the properties from Table 5 apply 2 3 3 Distinct Count Protocol The distinct count protocol computes the number of distinct values of an attribute or in other words the number of items in the aggregate data with a non zero count It accepts local distributions as input but the input peers internally only share a single bit 1 for item present and 0 for item not present Table 8 shows a simple example for one round The distinct count protocol does not have specific properties If input verification is enabled the privacy peers verify that each element shared is indeed a bit value 0 or 1 2 3 4 Event Correlation Protocol The event correlation protocol allows private aggregation of arbitrary network events An event e is defined by a key weight pair e k w P This notion is generic in the sense that keys can be defined to represent arbitrary types of network events which are uniquely identifiable The key k could for instance be the source IP address of packets triggering IDS alerts or the source address concatenated with a specific alert type or port number It could also be the hash value of extracted malicious payload or represent a
11. 2 3 Protocol Specific Usage and Configuration Key mpc Description peerclass Specifies the class which implements input peer functionality The class must be available in the classpath The input and privacy peer classes for the different protocols are listed below Addition mpc additive AdditivePeer AdditivePrivacyPeer Entropy mpc entropy EntropyPeer EntropyPrivacyPeer Unique Count mpc uniqueCount UniqueCountPeer UniqueCountPrivacyPeer Event Correlation mpc weightedSetIntersection WsipPeer WsipPrivacyPeer Benchmark mpc benchmark BenchmarkPeer BenchmarkPrivacyPeer privacypeer Specifies the class which implements privacy peer functionality The class must class be available in the classpath inputdirectory Path to the input directory default input Only read on input peers outputdirectory Path to the output directory default output Only read on input peers inputtimeout The time in seconds peers wait for new input files in case no file is available for the next round default 300 Only read on input peers field Size of the field over which the secrets are shared This must be a prime number of sufficient size to represent all intermediate computation results default 2 25 degree The degree of the polynomials used for Shamir s secret sharing Default is m 1 2 where m is the number of connected privacy peers minpeers Minimum number of privacy peer
12. In combination with the class services DirectoryPoller it allows to eas ily implement flexible file reading functionality see for example the method AdditivePeer readNext InputFile References 1 Martin Burkhart and Xenofontas Dimitropoulos Privacy preserving distributed network trou bleshooting bridging the gap between theory and practice ACM Trans Inf Syst Secur 14 4 31 1 31 30 December 2011 2 Martin Burkhart Mario Strasser Dilip Many and Xenofontas Dimitropoulos SEPIA Privacy Preserving Aggregation of Multi Domain Network Events and Statistics In 19th USENIX Secu rity Symposium August 2010 3 Dilip Many Martin Burkhart and Xenofontas Dimitropoulos Fast Private Set Operations with SEPIA Technical report ETH Zurich April 2012
13. LidaqAdeAligl O90 01q Input peer 2 Privacy peer 2 Input peer 3 Privacy peer 3 Figure 5 Different protocol classes in action Each line corresponds to a socket connection Each protocol instance is run in a separate thread That is a privacy peer runs one thread for the communication with each input peer and each privacy peer 2 Create the privacy peer class 3 Create the protocol classes 4 Create a new message class for exchanging the initial shares the final computation result and any other protocol specific information between the input and privacy peers 5 If your protocol needs to store more information about the peers than the standard PeerlInfo class derive a new one extending Peerinfo In this tutorial you will build an MPC protocol where the input peers a b c share inputs a1 G2 a3 b1 b2 with the privacy peers and the privacy peers compute the products of the inputs a b cy a2 bg You should be able to complete this tutorial within 1 2 hours The complete code of the tutorial is available in the mpc tutorial package Also the sample configurations contain a pre configured set of input and privacy peers running the tutorial protocol Alternatively you can use the default settings of the SEPIA configuration editor see Section 3 to generate configuration files for 5 input and 3 privacy peers running the tutorial protocol At some points in this tutorial your IDE or comp
14. Multi domain network management Event re Distinct CLI tools sardetan Addition Entropy oi Lib P2P based Operations on secrets arany communication lt Figure 2 SEPIA architecture overview This document is organized as follows The usage and configuration of the CLI protocols is de scribed in Section To facilitate the generation of configuration files and key material for large numbers of input and privacy peers we created the configuration editor which is documented in Section Section 4 introduces programmers to the library with a step by step tutorial for creat ing a new SEPIA protocol For more theoretical background and a comprehensive performance evaluation of SEPIA please refer to 2 2 Running SEPIA protocols This section introduces eleven basic protocols provided by SEPIA Vidas 2 RUNNING SEPIA PROTOCOLS Addition Allows the addition of arbitrary vector data Input files could be his tograms or a sequence of volume metrics such as byte or packet counts for different protocols TCP UDP etc Entropy Computation Computes the Tsallis entropy a generalization of Shannon s entropy of an aggregate distribution Input for this protocol is a local distribution e g the distribution of the activity of IP addresses Unique Count Comp Computes the number of distinct unique values of an attribute e g port number or IP address Inp
15. ProtocolPeer class we then implement the method createInitialSharesMessage which uses generateInitialShares and getInitialSharesForPrivacyPeer to create the mes sage with initial shares for the privacy peer s private synchronized void createlnitialSharesMessage logger log Level INFO Creating message for first round send initial shares inputPeer generatelnitialShares messageToSend new TutorialMessage inputPeer getMyPeerlD myPeerlIndex messageToSend setSenderlIndex myPeerIndex messageToSend setTimeSlotCount timeSlotCount messageToSend setMetricCount metricCount messageToSend setlsInitialSharesMessage true messageToSend setShares inputPeer getinitialSharesForPrivacyPeer privacyPeerIndex The createInitialSharesMessage method we just implemented is called from the run method of the TutorialProtocolPeer class to create the shares before sending them away Step 6 Receive Shares The privacy peers now obviously need the capabilities to receive these shares The run method of the TutorialProtocolPrivacyPeerToPeer Class calls the receive Message method which again forwards the received message to its observer i e the privacy peer class Therefore we have to add code to the notificationReceived method of the Tutorial PrivacyPeer class to actually handle the message with the initial shares comments and logging omitted if object instanceof TutorialMessage TutorialMessage
16. Sep Lal axe User Manual and Developer s Guide Authors Martin Burkhart Dilip Many Manuel Widmer http www sepia ee ethz ch Version 0 9 1 May 4 2012 CONTENTS Contents 2 Running SEPIA protocols 2 Configuring Peers 3 Protocol Specific Usage and Configuration 2 3 1 Vector Addition Protocol Roy Po 2 1 Starting Peers 2 22000 2 3 2 Entropy Protocol a a a ee ee 2 3 3 Distinct Count Protocol 2 0 0 0 a 2 3 4 Event Correlation Protocol oaoa a a 2 3 0 lop K Protocol 2 2448 222 be ee ee 2 3 6 Set Intersection Protocol 2 3 7 _Set Union Protocol 2 3 8 Set Intersection Cardinality Protoco 2 3 9 Set Union Cardinality Protocol 2 3 10 Bloom Filter Weighted Set Intersection Protoco 2 3 11 Benchmark Protocol 2 4 Controlling Privacy of aComputation 2 5 Creation of SSL Keys and Certificates 3 SEPIA Configuration Editor 1 Quickstar 3 2 Graphical User Interface 0 3 3 The Generated Configuration 4 Tutorial Create Your Own Protocol 4 1 Basics 000202 e ee eee 2 Read Input Data Generate Send and Receive Shares 3 Computing Functions on Secrets 4 4 Send Receive and Write the Final Result to a File AA OMDOANDMD MN Ww N 2 1 INTRODUCTION Privacy Peers gt simulated TTP Domain 1 Loca
17. asses This section explains how to implement an MPC protocol using all these classes The general starting procedure of a SEPIA protocol is as follows The PeerStarter class creates initializes and runs the configured peer instances Then the instances connect to other peers and perform the implemented MPC protocol SEPIA uses the Observer design pattern to handle messages and to signal important events e g final results or exceptions Upon receiving a network message a protocol thread sends a notifica tion which contains the message to its observers The peer observer then handles the message In the following a message is received over a network connection while a notification is sent by observables to their observers SEPIA protocols are separated into several classes For example in the mpc tutorial package we have the following main classes 1 TutorialPeer 2 TutorialPrivacyPeer 3 TutorialProtocolPeer 4 TutorialProtocolPrivacyPeerToPeer 5 TutorialProtocolPrivacyPeerToPP 6 TutorialMessage A peer class TutorialPeer in general holds the state of an input peer and starts the protocols between itself and the privacy peers A privacy peer class TutorialPrivacyPeer stores the state of a privacy peer and starts the protocols between the privacy peer and the other input or privacy peers The three protocol classes TutorialProtocolPeer TutorialProtocolPrivacyPeerToPeer and TutorialProtocolPrivacyPeerToPP simply contai
18. at operators could do is installing SEPIA input peers in their premises Input peers take sensitive local data and share it over the group of SEPIA privacy peers The privacy peers PPs together simulate a trusted third party TTP That is each privacy peer gets only one share of each data item From this share the PP cannot derive any information about the original data Only if a majority of PPs comes together and combines their shares they can reconstruct the information In addition the PPs can perform any computation on the shared secrets without reconstructing intermediate values When the computation has finished the PPs reconstruct the final result for instance all intrusion alerts reported by three or more networks and reveal it to the input peers These will then use this information for improving network management SEPIA s overall architecture is shown in Fig 2 SEPIA consists of a library and a set of command line CLI tools The library implements basic operations on shared secrets such as addition multiplication and comparison of values Furthermore it provides functionality to connect partici pants of the computation On top of the basic library four CLI tools are implemented These tools implement ready to use high level protocols such as the aggregation of arbitrary network events and the computation of common network statistics 1SEPIA stands for Security through Private Information Aggregation Applications
19. can be seen in Table 11 2 3 10 Bloom Filter Weighted Set Intersection Protocol The set intersection protocol takes as input a file describing the key set and their weights with one set element and associated weight per line The key can be any string not containing a semi colon terminated by a semi colon The weight is an integer right after the semi colon The keys are stored in a counting bloom filter CF K and the weights are stored in CFW by inserting each key as many times as the value of its associated weight Both filters CFK and CFW are then shared among the PPs From this the PPs compute a bloom filter with each position u satisfying eo Y CFW u if CFK u gt te and Y CFW u gt ty 6 Fwsr u 0 otherwise If the weights of the keys satisfying the above requirement shall not be learnt the resulting filter is computed to satisfy Fwsr u 1 if SSCFK u gt t and gt CFW u gt tw Fwsr u 0 otherwise 7 2 4 Controlling Privacy of a Computation 13 Key mpc bfwsi Description keythreshold The minimum total peer count an event needs to have in order to be recon structed weightthreshold The minimum total weight an event needs to have in order to be reconstructed learnweights Defines whether the weights shall be reconstructed true or not false Table 12 Specific properties Bloom Filter Based Weighted Set Intersection Protocol Key mpc bench Description
20. ch the algorithm searches for the value r sep arating the k th from the k 1 th weight value Adjusting this parameter speeds up the protocol execution But a too low value will cause the protocol to output an incorrect result The protocol supports two input file formats selected by the inputType property For backwards compatibility the default input format is BasicMetricsBinary This file format is used by a pro prietary tool for binary port and full IPv4 distributions as input Usage of the TopkKeyWeightData format is recommended Input files in the TopkKeyWeightData format the protocol parses as fol lows Each row contains comma separated values The first value in each row is used as the event key and the second is used as the weight of the event The only difference to the event correlation protocols keyWeightData format is that the TopkKeyWeightData format only supports integer keys and weights while the former supports longs Output files contain one reconstructed item per line with the following format lt key gt lt aggregated weight gt 2 3 Protocol Specific Usage and Configuration 11 Key Description bloomfilter iscounting Defines whether counting Bloom filters shall be used true or not false for binary Not used by Bloom filter based weighted set intersection protocol hashcount The number of hash functions to be used for the Bloom filter size The size of the Bloom filter to be used Table 1
21. change their information However if we configure 9 privacy peers a group of 5 privacy peers must collude to achieve the same So by setting a minimum number of required privacy peers we can adjust the collusion threshold Polynomial Degree Another parameter useful for controlling data privacy is mpc degree It controls the degree of the polynomials used in Shamir s secret sharing By default it is set to m 1 2 with m being the number of privacy peers This setting yields the above privacy proper ties In general any set of t 1 or more privacy peers can interpolate the secret This means if we sett m 1 then all privacy peers must be available for interpolating a secret and no subgroup of colluding privacy peers could break the scheme While this raises the collusion threshold it makes private multiplication impossible For private multiplication to work m gt 2t 1 must hold 2 In summary t m 1 2 is the highest collusion threshold we can choose if the protocol requires multiplication or any operation built on top of multiplication such as comparison However if the protocol only requires addition the collusion threshold can be set arbitrarily 2 5 Creation of SSL Keys and Certificates This section describes how key pairs certificates and symmetric keys can be created For an SSL connection the server needs to authenticate whereas client authentication is optional In SEPIA protocols both parties need a
22. configuration with protocol specific properties they won t be displayed in the GUI but they will be considered for the configuration There is no need to enter them again If you need to change some just enter the key value pair s into the provided box and the loaded property will be overwritten when creating or saving the current configuration Full PKI If you choose to generate a full PKI you need to make sure that the keytool of the java installation is in the path The configuration editor will read input for the keytool from the provided input file Please look at the comments in that file for further instructions on the required input Unfortunately keytool is language dependent so the very last input of the tuple which is a 3 3 The Generated Configuration S SEPIA Configuration E 17 File General Options I Advanced Options Input Generation mpc peerclass Specify the jar file of your protocol tutorial jar Min Generate Min minimum peers to start the mpc Number of InputPeers 3 5 Generate peer configs that will be created Number of PrivacyPeers 3 B Protocol specific properties peers numberoftimeslots p 0 peers numberofitemsintimeslot mpc privacypeerclass v Allow multiple peers on the same host v Test if enough hosts are online ping hosts from PP IPhosts txt C Verbose logging on all input amp privacy peers C Generate full PKI or use the provided testKeys if u
23. elects the graphical interface whereas c chooses command line operation The last parameter specifies the configuration file to be used By default config properties is used to configure the settings The default configuration generates a set of 5 input and 3 privacy peers running the tutorial protocol see Section 4 There are many comments in the file to help you with the configuration You can also specify another config file to be used The command would then look as follows java jar MakeConfig jar c someOther properties 3 2 Graphical User Interface Figure 4 shows a screenshot of the GUI The file menu allows saving and opening different con figurations To generate a configuration click the Create button at the bottom of the window The Advanced Options tab allows you to configure additional properties The Input Generation tab al lows the automatic generation of random input data for protocols The number of files produced will match the peers numberoftimeslots property and there will be peers numberofitemsintimeslot entries in one file There are 2 options for generating input 1 Set Generation will produce sets of strings formatted like IPv4 addresses and separated by newlines 2 Random Numbers will just produce random numbers which are separated by the specified delimiter All Options of the GUI can also be directly changed in the config properties file Protocol specific properties When you open a
24. esultEvent See next step writeOutputToFile if currentTimeSlot lt timeSlotCount currentTimeSlot initializeNewRound else protocolStopper setlsStopped true else send exception event From what you know by now the code should be self explanatory Step 10 Write Results to File The last thing left to do is writing the results to a file To that end we create the writeResultToFile method in TutorialPeer and call it from within the notificationReceived method line 19 in the above code OCOONDOARWND tt a protected void writeOutputToFile throws Exception String fileName outputFolder tutorial_output_ String valueOf getMyPeerlID replace _ _round currentTimeSlot csv StringBuilder line new StringBuilder for int resultIndex 0 resultIndex lt finalResults length resultIndex line line append finalResults resultIndex append Services writeFile line substring 0 line length 2 fileName This code just writes the products separated by commas in one line to the file The file name includes the input peer s ID and the current round number 26 REFERENCES A final note If your data is vector oriented as in the example above you might want to use the class mpc VectorData as a container for both input and output data It also provides methods for reading and writing to from files
25. f this protocol is the same as for the set intersection protocol It also first computes the set intersection but without reconstructing the filter representing the intersection set The PPs take this filter and add up the individual positions bits or counters The resulting sum is reconstructed and sent back to the IPs The IPs then compute an estimate of the intersection set cardinality The output text file contains in the first line a human readable text describing the contents of the next three lines The following lines contain the sum of the input filter positions the sum of the intersection filter positions and the intersection set cardinality estimate The configuration options of this protocol can be seen in Table 11 2 3 9 Set Union Cardinality Protocol The input of this protocol is the same as for the set union protocol It also first computes the set intersection but without reconstructing the filter representing the union set The PPs take this filter and add up the individual positions bits or counters The resulting sum is reconstructed and sent back to the IPs The IPs then compute an estimate of the union set cardinality The output text file contains in the first line a human readable text describing the contents of the next three lines The following lines contain the sum of the input filter positions the sum of the union filter positions and the union set cardinality estimate The configuration options of this protocol
26. ics jar Note that on Linux the character for separating entries in the classpath option cp is the colon while on Windows it is the semicolon Make sure to use a Java VM with version at least 1 5 The following options are available v Enable verbose logging mode This creates quite big log files help Show usage information p lt peerType gt Specifies the peer type 0 input peer 1 privacy peer c lt configFile gt Path to the configuration file Thus an input peer with verbose logging can be started by the following command java cp sepia jar application jar MainCmd v p 0 c MyConfig properties Note that more information is needed to configure SEPIA peers SEPIA reads the detailed configu ration from the configuration file parameter c All configuration options are described in the next sections 2 2 Configuring Peers SEPIA configuration files are organized as a sequence of lt key gt lt value gt lines The key iden tifies a property to set Fig 3 shows a complete example of a configuration file for an input peer 6 2 RUNNING SEPIA PROTOCOLS Key peers Description mypeerid A peer s unique ID String value minpeers The minimun number of input peers that must be present before the computa tion starts timeout Before each round a phase of connection discovery is performed This is the active timeout in milliseconds when waiting for new connections
27. iler might complain that some classes or their methods are unknown To resolve these problems just add the appropriate imports e g import events FinalResultEvent 4 1 Basics The following pages guide you through step by step instructions that develop a full blown protocol starting from the stub classes in the mpc skeleton package see tutorial jar All code fragments shown are available in the corresponding classes of the mpc tutorial package Step 1 Base Classes First choose a name for your protocol e g Tutorial The package for the tutorial protocol classes should then be named tutorial The class names should start with Tutorial Copy the classes from the mpc skeleton package to your own package and rename them appropriately oOuahWNM ai AON ere RON 4 1 Basics 21 Afterwards do case sensitive replacements or renaming with refactoring within all files e Skeleton gt Tutorial e skeleton gt tutorial e SKELETON gt TUTORIAL Step 2 Starting the protocol Your new protocol implementation will likely reside in a separate jar file Therefore you need to tell SEPIA how to instantiate the input and privacy peer classes This can be done by configuring the peer classes in the config files mpc peerclass mpc tutorial TutorialPeer mpc privacypeerclass mpc tutorial TutorialPrivacyPeer Make sure that the sepia jar and the classes TutorialPeer and TutorialPrivacyPeer are avail able in the cla
28. ivacyPeer startFinalResultReconstruction ppThreadsBarrier await if doOperations logger severe Final result reconstruction failed returning return After the results are reconstructed we store them in an array on the TutorialPrivacyPeer and let the protocol threads which communicate with the input peers broadcast the results public void setFinalResult finalResults new long operationIDs length for int i 0 i lt operationIDs length i finalResults i primitives getResult operationIDs i 0 startNextPeerProtocolStep The call of startNextPeerProtocolStep in line 6 opens the barrier at which all the Tutorial ProtocolPrivacyPeerToPeer threads are waiting see the respective run method Again this method has to be called by the run method of TutorialProtocolPrivacyPeerToPP if ppThreadsBarrier await 0 privacyPeer setFinalResult logger log Level INFO Tutorial protocol round completed 4 4 Send Receive and Write the Final Result to a File Step 8 Send Final Results Next we complete the sendFinalResults method in the Tutorial ProtocolPrivacyPeerToPeer Class by setting the final results in the message I ee messageToSend setResults privacyPeer getFinalResult PA ead The getFinalResult method of the privacy peer class is simply a getter method 4 4 Send Receive and Write the Final Result to
29. keysize 2048 The user will then be prompted to specify his name organization 15 Export to a certificate file The self signed certificate is then exported to a certificate file This can be given to the other party keytool export alias privacypeer0iAlias storepass privacypeer01StorePass file privacypeer01iCertificate crt keystore privacypeer01iKeyStore jks Import trusted certificate Other parties certificate can be imported to a local keystore and labeled as trusted In the example bellow the certificate of privacy peer 1 is imported to the keystore of privacy peer 2 keytool import v trustcacerts alias privacypeer01lAlias file privacypeer01iCertificate crt keystore peer02KeyStore jks storepass privacypeerO2StorePass The following command shows a list of all key pairs and trusted certificates stored in a key store keytool list keystore peer01KeyStore jks storepass peer0iStorePass Command to Run Programs that Use Keystores The truststore used can be set by setting the system property System setProperty javax net ssl trustStore peer01KeyStore jks If however several instances run on the same machine e g for testing reasons the truststore will have to be set in the command line java Djavax net ssl trustStore peer0iKeyStore jks lt peer arguments gt 3 SEPIA Configuration Editor Creating configuration files and certificates keystores for large numbers of input and privacy peers
30. l data Input Peer o gt g Local data Input Peer Domain 2 Secret Sharing Aggregate i g 3 statistics oO y 3 5 a Local data Input Peer Private data Secret data Public data known to a single domain nobody knows known to all domains Figure 1 SEPIA deployment scenario The privacy peers simulate a trusted third party TTP 1 Introduction SEPIA is a Java library for secure multiparty computation MPC It is designed to achieve high performance for parallel execution of private operations A typical deployment scenario for SEPIA is depicted in Fig 1 A number of independent network domains are observed by some type of network probe The probe could for instance produce NetFlow records SNMP inventory data or IDS alert logs Although the individual network data is sensitive and therefore kept secret the networks could substantially benefit by aggregating their data with data from other networks for alert correlation collaborative anomaly detection multi domain traffic engineering or collecting network performance statistics For example they might be interested in whether other networks see similar intrusion alerts or not Yet they would never just hand over their alerts But if a network operator knew that indeed other networks are attacked by the same group of hackers they might be willing to cooperate in order to come up with a more effective collaborative defense Wh
31. lications or compar isons in parallel and reports execution time and data volume statistics This allows assessing the performance of a specific setup Benchmark All protocols work on a continuous data stream partitioned into time windows That is for each time window input peers expect a separate input file to be dropped in the input directory If no file is available they will poll the directory until the expected file arrives As soon as all inputs are available for the current window a new computation round is started When the round has finished the aggregated output for that time window is written to a file Please note that you can find complete sample scenarios for all protocols except for Benchmark in the archive sampleConfigurations zip which can be downloaded from the SEPIA web page Each scenario contains one folder per input and privacy peer with all the necessary input data configuration files and startup scripts All peers are configured to run on localhost on a unique port In order to distribute the peers just copy the corresponding folder to a remote machine and update the peer s address in all config files see Section 2 2 For instance in order to run the scenario for unique count computation just enter the following commands cd sampleConfigurations uniqueCount runAll sh This will start the input and privacy peers After a connection discovery phase the peers perform the unique count protoc
32. n the run functions that start the protocol steps in the appropriate order Each of the protocol classes governs a different part of the communication The first protocol class TutorialProtocolPeer handles the communication of the input peer with a privacy peer At the other end the second protocol class TutorialProtocolPrivacyPeerToPeer handles the communication of the privacy peer with an input peer Lastly the third protocol class TutorialProtocolPrivacyPeerToPP handles the communication between two privacy peers For each type of connection the corresponding instance is run in a separate thread This minimizes synchronization delays and allows the parallelization of the communication and local computation for each peer Fig 5 illustrates the role of the different classes Note that input peers do not directly communicate with each other The methods implementing the protocol steps are mainly implemented in the peer classes because they require access to the state information and sometimes need to be synchronized among the different protocol threads Additionally a message class TutorialMessage holds data to be sent between input and privacy peers such as the initial shares or the final computation result The following steps are required to implement a new protocol 1 Create the input peer class 20 4 TUTORIAL CREATE YOUR OWN PROTOCOL ProtocolPrivacyPeer ToPeer Privacy peer 1 ProtocolPeer yP Input peer 1 ddO
33. nchecked Must be in same folder as MakeConfig jar 2000 mpc tutorial TutorialPeer mopc tutorial TutorialPrivacy v Make runAll sh silent no console output Figure 4 Screenshot of the graphical user interface of the SEPIA configuration editor confirmative yes must be supplied in the correct language e g ja for the German version or the keytool will hang Every host will then have his own private key and a keystore where the public keys of the other hosts are saved Passwords for keystores and keys can be found in the config file final properties of the respective configuration 3 3 The Generated Configuration Table 15 shows the files produced when the configuration was successfully created Filename Description hostnamelP PP folder containing the files below input folders containing input data of an input peer final properties perfomance sh startup bat startup sh properties that will be used to start the peer script to log top during runtime CPU amp memory consumption Windows script to start the peer Linux script to start the peer distributeConfigs sh extractStatistics sh RunAll bat runAll sh Script to copy the configurations to the corresponding hosts Script to extract information about the protocol runtime from log files after a run Script to start a localhost config on Windows Script to start a configuration on Linux system
34. ns and all of them then perform the computation doOperations After the computation shares of the product can be retrieved using the primitives getResult method As the last step of the overall computation explicit reconstruction operations have to be scheduled For this we add the following code to the privacy peer class OAONDARWN ao ou ARUN RON NOoORONM OMNDARWON won 24 4 TUTORIAL CREATE YOUR OWN PROTOCOL public void startFinalResultReconstruction long result new long operation IDs length for int i 0 i lt operationIDs length i result i primitives getResult operationIDs i 0 initializeNewOperationSet result length operationIDs new int result length long data null for int i 0 i lt result length i operationIDs i i data new long 1 data 0 result i primitives reconstruct operationIDs i data The code is very similar to the code of the startProductComputations method The main differ ences are that we use the getResult method first to get the result from the completed operations using their IDs and that we create reconstruct operations instead of multiplication operations Note that retrieving the results must always be done before initializing a new operation set Otherwise the results are lost This method is then called from the run method of TutorialProtocolPrivacyPeerToPP if ppThreadsBarrier await 0 pr
35. ocol round starts the peers perform a connection discovery phase until enough input privacy peers are available Since version 0 9 SEPIA is robust against peer failureg That is it can continue computation in case input privacy peers fail and disconnect When a peer disconnects during an ongoing com putation SEPIA checks if the minimum conditions are still met If not enough peers are available computation is stopped Number if Input Peers By requesting a minimum number of input peers it is guaranteed that the final result is sufficiently aggregated and that individual contributions are hidden in the aggregate result Consider for example if you ran the vector addition protocol with just two input peers Then each input peer can derive the other peer s input data from the final result and its own input data If you set the minimum number of input peers to 5 however this is not possible This must not be confused with security against malicious adversaries In SEPIA adversaries are assumed to be semi honest and a failure is not intentional Failures are e g caused by a server crash or network disruption 14 2 RUNNING SEPIA PROTOCOLS Number of Privacy Peers Likewise privacy of input data is better protected if more privacy peers participate in the computation SEPIA is secure as long as the majority of the privacy peers is honest If there are only 3 privacy peers the system is broken as soon as any two collude and ex
36. ol for 5 rounds and each input peer stores the resulting aggregated output in its local output folder one file per round More protocol specific information such as input file format and configuration can be found in Section 2 3 2 1 Starting Peers 5 Peer peers mypeerid peer01 peers minpeers 3 peers timeout 10000 peers numberoftimeslots 5 peers numberofitemsintimeslot 5 peers activeprivacypeers pp01 localhost 50001 pp02 localhost 50002 pp03 localhost 50003 Connection connection keystore peer0ikeyStore jks connection keystorepassword peer01StorePass connection keypassword peer0ikeyPass connection keystorealias peer0lalias MPC mpc inputdirectory input mpc outputdirectory output mpc inputtimeout 300 mpc peerclass mpc additive AdditivePeer mpc privacypeerclass mpc additive AdditivePrivacyPeer mpc minpeers 3 mpc field 3775874107000403461 mpc skipinputverification false mpc maxelement 5400000000 Figure 3 Example configuration file for an input peer in the addition protocol For more examples see the scenarios in folder sampleConfigurations 2 1 Starting Peers The Syntax for starting SEPIA input and privacy peers from command line is as follows java cp sepia jar protocol jar MainCmd options protocol jar contains the protocol implementation and varies depending on the scenario For instance the addition entropy and distinct count protocols are stored in statist
37. s Protocol specific configuration is discussed in the next section Properties with a default value are optional whereas the remaining are mandatory There are three kinds of properties First basic properties configuring the input and privacy peers with prefix peers are described in Table 1 Secondly properties configuring the connection with prefix connection are listed in Table 2 Lastly properties configuring the MPC protocol with prefix mpc are shown in Table 3 2 3 Protocol Specific Usage and Configuration In the following we describe protocol specific configuration parameters as well as input and output file formats for the five protocols To see the protocols in action with concrete data please refer to the sample configurations For entropy and distinct count computation the examples use input vectors with a size of 65 535 elements using port histograms as input 2 3 1 Vector Addition Protocol The vector addition protocol simply takes a list of integer values as input All the participating input peers contribute a vector of comma separated values in each computation round The goal of the computation is to privately compute the sum of the vectors as shown in Table 4 That is no peer learns the input values of other peers but every peer learns the sum of the input vectors Each vector element could for instance be an additive metric such as the local flow or byte count for different protocols and or directions
38. s distributed or local Table 15 Files generated by the SEPIA configuration editor 18 3 SEPIA CONFIGURATION EDITOR First you need to distribute the configuration to all the hosts If you are working in a Linux environ ment use distributeConfigs sh for that purpose it will automatically copy all necessary files to the correct hosts using SCP Else you need to copy the files sepia jar yourprotocol jar testConfigKey Store jks toprc ConfigurationFolder to the corresponding hosts Starting a Configuration To start a configuration you can use the runAll sh script On Windows RunAll bat will only work if all peers run on the same computer localhost Another way to get your protocol running is to start each peer manually using startup bat or startup sh in the configuration folder 19 4 Tutorial Create Your Own Protocol SEPIA distinguishes between input and privacy peers In this section the term peer will refer to both input and privacy peers as a collective term Basic operations on Shamir shares are im plemented in separate classes in the package mpc protocolPrimitives operations There are more than a dozen operations including the most important ones namely multiplication equality testing and less than comparison For a complete overview of the different classes please refer to the Javadoc These operations are used via the Primitives class To make usage easier the mpc protocolPrimitives package provides some helper cl
39. s s per input peer and round default 10 perpeer mintotalweight The minimum total weight T an event needs to have in order to be recon threshold structed default 100 minfeaturecount The minimum total peer count T an event needs to have in order to be recon threshold structed default 2 featureduplicates Enables the input verification for duplicate event keys true false default check true maxweightcheck Enables the input verification for maximum weights true false default true inputType Selects the input file format weightByCount keyWeightData default weightByCount Table 9 Specific properties Event Correlation Protocol Key mpc topk Description k Defines the number of top features to compute s Defines the number of hash arrays used h The size of the hash arrays seed The common seed used for the hash functions maxtau The maximum value that is considered in the binary search for the value tau separating the k th from the k 1 th value inputType Selects the input file format BasicMetricsBinary TopkKeyWeightData default BasicMetricsBinary Table 10 Specific properties Top k Protocol The PPs then reconstruct the k keys with the largest aggregate weights and their weights and send it to the IPs Table 10 lists the protocol specific configuration options The special parameter maxtau indicates the upper bound on the range 0 maxtau in whi
40. s that have to be present for the computation to start skipinput Defines whether input verification should be skipped true or not false De verification fault false The type of input verification that is performed depends on the protocol If verification is enabled input peers with non compliant data are ex cluded paralleloper The maximum number of MPC operations that are performed in parallel For no ationscount limit specify 0 default 0 Table 3 MPC parameters for input and privacy peers mpc Input Peer 1 421706 6393885 4262205881 554130 6522044 Input Peer 2 517974 1234433 7947344550 345443 6345454 Input Peer 3 238220 5002015 4899900381 200033 7653329 Output 1177900 12630333 17109450812 1099606 20520827 Table 4 Example round of the addition protocol Table 5 gives an overview of all the protocol specific properties of the vector addition protocol If input verification is enabled it is possible to require each vector element to be below a maximum value Notice that if too many input peers are disqualified due to invalid inputs the output file will contain the result vector of the input verification rather than the vector addition result vector which wasn t computed These two vectors don t have the same length and can therefore be easily distinguished Also the log file will contain a warning 2 3 2 Entropy Protocol The Tsallis entropy is a generalization of Shannon s entropy that has applica
41. sspath To include all the necessary files in the classpath you can use the cp option of the java VM and then start each SEPIA peer via the MainCmd class java cp sepia jar tutorial jar MainCmd Your new protocol is already running but at this point it does not do anything useful yet Step 3 Variable Initialization To track the state of the computation and hold local data we need some variable definitions Add the following definitions to the TutorialPeer class xx indicates if the initial shares were generated yet private boolean initialSharesGenerated false xx array containing the input data of this peer for all time slots format inputIndex protected long inputData null x array containing my initial shares dimensions numberOfPrivacyPeers numberOfltems private long initialShares null Then add the following variable definition to the TutorialBase class xx contains the final results protected long finalResults null To initialize these variables add the following code to the initializeNewRound method of the TutorialPeer class only shown in parts initialSharesGenerated false initialShares null finalResultsToDo numberOfPrivacyPeers finalResults null The privacy peer class also needs a variable definition xx number of initial shares that the privacy peer yet has to receive private int initialSharesToReceive 0
42. such element gets written to the output file one per line The configuration options of this protocol can be seen in Table 11 2 3 7 Set Union Protocol The set union protocol takes as input a file describing a set of items with one set element per line just like the intersection protocol From the input set the IPs compute their bloom filter representation of their set The filters are then sent to the privacy peers The privacy peers then compute the bloom filter representing the intersection set position wise That is for all positions u with 1 lt u lt s they perform the logical OR BF u BFi u V BF2 u V V BF u 4 For the multiset case the equation is as follows BFY u BF u BFo u BF u 5 All positions of BF are then reconstructed and sent back to the input peers The output files of the input peers contain the union bloom filter The first line is a human readable text describing the next three lines The following 3 lines contain the configurable properties of a bloom filter The second line is a Boolean indicating if the filter is a counting bloom filter the 12 2 RUNNING SEPIA PROTOCOLS third contains the number of hash functions and the fourth the filter length These lines are directly followed by the content of the filter one line per position The configuration options of this protocol can be seen in Table 11 2 3 8 Set Intersection Cardinality Protocol The input o
43. t peerlndex 0 peerlndex lt numberOflnputPeers peerlndex long initialShares getPeerlnfoBylndex peerlndex getInitialShares if initialShares null only consider active input peers data datalndex initialShares operationIndex primitives product operationIndex data This method is executed once per round When using the protocol primitives with the helper classes we first have to initialize a new operation set with the number of operations we want to run in parallel line 3 After that we create a new integer array with the operation IDs The IDs start at 0 and are consecutive These IDs are later used for fetching the results of each operation On line 8 the data array is created for holding each input peer s share The actual product oper ations are scheduled within the for loop by invoking the respective method on the primitives with the operation s ID and input data line 16 The above method is called from the run method of the TutorialProtocolPrivacyPeerToPP class shown only in parts if ppThreadsBarrier await 0 compute and reconstruct products privacyPeer startProductComputations ppThreadsBarrier await if doOperations logger severe Computing products failed returning return The ppThreadsBarrier is provided by the privacy peer to synchronize all threads One thread always prepares the computations calls startProductComputatio
44. tions in anomaly de tection The 1 parametric Tsallis entropy is defined as mY 1 5 0 1 k 8 2 RUNNING SEPIA PROTOCOLS Key Description mpc maxelement Specifies the maximum value of a vector element that is accepted as valid input default 23 This property is only checked if input verification is not skipped see property mpc skipinputverification in Table f Table 5 Specific properties Vector Addition Protocol Input Peer 1 1 0 7 10 5 Input Peer 2 0 2 5 0 0 Input Peer 3 2 2 0 0 5 Intermediate secret 3 4 12 10 10 Output q 2 0 7573964 Table 6 Example round of the entropy protocol and has a direct interpretation in terms of moments of order q of the distribution In particular the Tsallis entropy is a generalized non extensive entropy that up to a multiplicative constant equals the Shannon entropy for q gt 1 The entropy protocol takes a local distribution as input which is given in a comma separated list of item counts similarly to the addition protocol In a first step the input distributions are privately summed to derive an aggregate distribution Up to this point the protocol is identical to the vector addition protocol But after this point instead of reconstructing the aggregate distribution the counts for each item k are used to privately derive the probabilities used in 1 and eventually the Tsallis entropy H of the aggregate distribution See
45. tribute its information and collaborate with the other peers reporting the same event Likewise a peer might only be interested in reconstructing events of a certain impact having a non negligible aggregated weight For more details on the protocol please refer to 2 For input verification it is possible to have the privacy peers verify that each input peer reports unique event keys i e it reports each event only once In addition the peers can specify a max imum event weight Each of these checks is enabled by a separate configuration option The general property mpc skipinputverification is ignored Table 9 lists the protocol specific config uration options The field size property mpc field is overridden and determined internally based on the maximum event key maxfeature and the maximum expected sum of weights number of input peers times the maximum weight maxweight For an example of the event correlation protocol please look at the sample configurations The protocol supports two input file formats selected by the inputType property The protocol parses input files in the weightByCount format as follows Each row contains comma separated values The first value in each row is used as the event key The number of lines in which this key occurs is used as the weight count of the event Input files in the keyWeightData format the protocol parses as follows Again each row contains comma separated values The first value in each row
46. ut is again a local distribution Event Correlation Correlates arbitrary events defined by a key and a weight It recon structs only the events that are reported by a minimum number of input peers and have a minimum aggregated weight Top K Computes the aggregated weight of events defined by a key and a weight to then compile a list of top k events with the k largest aggre gated weights It reconstructs only the top k events Set Intersection This protocol and the next four are based on bloom filters It computes the intersection of multiple sets Input is a set of items that are con verted into a bloom filter representation It reconstructs the bloom filter representation of the intersection set and outputs the elements in the intersection Set Union Computes the union of multiple sets It reconstructs and outputs the bloom filter representation of the union set Set Intersection Cardi nality Computes the cardinality of the set intersection Set Union Cardinality Computes the cardinality of the set union Bloom Filter Weighted Set Intersection Like the event correlation protocol it correlates arbitrary events defined by a key and a weight It reconstructs only the events that are reported by a minimum number of input peers and have a minimum aggregated weight This version uses bloom filters contrary to the event correlation protocol Performs a number of basic MPC operations multip
47. uthentication Several ways are possible for generating private public key pairs and certificates e g a trusted certification authority CA might create certificates For reasons of simplicity self signed certificates can be used and exported Each party who wants to communicate must export its public key and certificate and the other party needs to import them as trusted certificates The following describes how key pairs and certificates are created and exchanged using the JDK s keytool A privacy peer privacypeer01 generates and exports the keys and the certificate and an input peer peer01 imports it The configuration editor described in Section 3 automates these steps and generates a full PKI for all the configured input and privacy peers NOTE To reduce the key generation effort for simple tests with many input and privacy peers all peers can use the same key The sample configuration for the event correlation protocol contains a KeyStore pKeyStore jks which trusts its own key Thus all input and privacy peers can use the same KeyStore file Note however that this is not secure In production environments make sure that keys and certificates are properly configured Creation of key pairs Using the keytool a self signed private public key pair is generated keytool genkey v keystore privacypeer01iKeyStore jks storepass privacypeerO1StorePass alias privacypeer01Alias keypass privacypeer01KeyPass keyalg RSA
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