Nghiên cứu xây dựng một số giải pháp đảm bảo an toàn thông tin trong quá trình khai phá dữ liệu

B GIÁO , mn D C VÀ ÀO T O B QU C PHÒNG VI N KHOA H C VÀ CÔNG NGH QUÂN S - L - NG TH D NG DISTRIBUTED SOLUTIONS IN PRIVACY PRESERVING DATA MINING (Nghiên c u xây d ng m t s gi i pháp đ m b o an toàn thơng tin q trình khai phá d li u) LU N ÁN TI N S TOÁN H C Hà N i - 2011 B GIÁO D C VÀ ÀO T O B QU C PHÒNG VI N KHOA H C VÀ CÔNG NGH QUÂN S - L - NG TH D NG DISTRIBUTED SOLUTIONS IN PRIVACY PRESERVING DATA MINING (Nghiên c u xây d ng m t s gi i pháp đ m b o an tồn thơng tin q trình khai phá d li u) Chuyên ngành: B o đ m toán h c cho máy tính h th ng tính tốn Mã s : 62 46 35 01 LU N ÁN TI N S TOÁN H C Ng i h ng d n khoa h c: GIÁO S - TI N S KHOA H C H TÚ B O PHÓ GIÁO S - TI N S B CH NH T H NG Hà N i - 2011 Pledge I promise that this thesis is a presentation of my original research work Any of the content was written based on the reliable references such as published papers in distinguished international conferences and journals, and books published by widely-known publishers Results and discussions of the thesis are new, not previously published by any other authors i Contents INTRODUCTION 1.1 Privacy-preserving data mining: An overview 1.2 Objectives and contributions 1.3 Related works 1.4 Organization of thesis 12 METHODS FOR SECURE MULTI-PARTY COMPUTATION 13 2.1 Definitions 13 2.1.1 Computational indistinguishability 13 2.1.2 Secure multi-party computation 14 2.2 Secure computation 15 2.2.1 Secret sharing 15 2.2.2 Secure sum computation 16 2.2.3 Probabilistic public key cryptosystems 17 2.2.4 Variant ElGamal Cryptosystem 18 2.2.5 Oblivious polynomial evaluation 20 2.2.6 Secure scalar product computation 21 2.2.7 Privately computing ln x 22 PRIVACY PRESERVING FREQUENCY-BASED LEARNING IN 2PFD SETTING 24 3.1 Introduction 24 3.2 Privacy preserving frequency mining in 2PFD setting 27 3.2.1 Problem formulation 27 3.2.2 Definition of privacy 29 3.2.3 Frequency mining protocol 30 ii 3.3 3.4 3.5 3.2.4 Correctness Analysis 32 3.2.5 Privacy Analysis 34 3.2.6 Efficiency of frequency mining protocol 37 Privacy Preserving Frequency-based Learning in 2PFD Setting 38 3.3.1 Naive Bayes learning problem in 2PFD setting 38 3.3.2 Naive Bayes learning Protocol 40 3.3.3 Correctness and privacy analysis 42 3.3.4 Efficiency of naive Bayes learning protocol 42 An improvement of frequency mining protocol 44 3.4.1 Improved frequency mining protocol 44 3.4.2 Protocol Analysis 45 Conclusion 46 ENHANCING PRIVACY FOR FREQUENT ITEMSET MINING IN VERTICALLY 49 4.1 Introduction 49 4.2 Problem formulation 51 4.2.1 Association rules and frequent itemset 51 4.2.2 Frequent itmeset identifying in vertically distributed data 52 4.3 Computational and privacy model 53 4.4 Support count preserving protocol 54 4.4.1 Overview 54 4.4.2 Protocol design 56 4.4.3 Correctness Analysis 57 4.4.4 Privacy Analysis 59 4.4.5 Performance analysis 61 Support count computation-based protocol 64 4.5.1 Overview 64 4.5.2 Protocol Design 65 4.5.3 Correctness Analysis 65 4.5.4 Privacy Analysis 67 4.5.5 Performance analysis 68 Using binary tree communication structure 69 4.5 4.6 iii 4.7 Privacy-preserving distributed Apriori algorithm 70 4.8 Conclusion 71 PRIVACY PRESERVING CLUSTERING 73 5.1 Introduction 73 5.2 Problem statement 74 5.3 Privacy preserving clustering for the multi-party distributed data 76 5.4 5.5 5.3.1 Overview 76 5.3.2 Private multi-party mean computation 78 5.3.3 Privacy preserving multi-party clustering protocol 80 Privacy preserving clustering without disclosing cluster centers 82 5.4.1 Overview 83 5.4.2 Privacy preserving two-party clustering protocol 85 5.4.3 Secure mean sharing 87 Conclusion 88 PRIVACY PRESERVING OUTLIER DETECTION 91 6.1 Introduction 91 6.2 Technical preliminaries 92 6.2.1 Problem statement 92 6.2.2 Linear transformation 93 6.2.3 Privacy model 94 6.2.4 Private matrix product sharing 95 Protocols for the horizontally distributed data 95 6.3.1 Two-party protocol 97 6.3.2 Multi-party protocol 100 6.3 6.4 Protocol for two-party vertically distributed data 101 6.5 Experiments 104 6.6 Conclusions 106 SUMMARY 107 Publication List 110 Bibliography 111 iv List of Phrases Abbreviation Full name PPDM Privacy Preserving Data Mining k-NN k-nearest neighbor EM Expectation-maximization SMC Secure Multiparty Computation DDH Decisional Diffie-Hellman PMPS Private Matrices Product Sharing SSP Secure Scalar Product OPE Oblivious polynomial evaluation ICA Independent Component Analysis 2PFD 2-part fully distributed setting FD fully distributed setting c ≡ computational indistinguishability v List of Tables 4.1 The communication cost 62 4.2 The complexity of the support count preserving protocol 63 4.3 The parties’s time for the support count preserving protocol 64 4.4 The communication cost 68 4.5 The complexity of the support count computation protocol 69 4.6 The parties’s time for the support count computation protocol 70 6.1 The parties’s computational time for the horizontally distributed data 105 6.2 The parties’s computational time for the vertically distributed data 105 vi List of Figures 3.1 Frequency mining protocol 33 3.2 The time used by the miner for computing the frequency f 38 3.3 Privacy preserving protocol of naive Bayes learning 41 3.4 The computational time for the first phase and the third phrase 43 3.5 The time for computing the key values in the first phase 43 3.6 The time for computing the frequency f in third phrase 44 3.7 Improved frequency mining protocol 47 4.1 Support count preserving protocol 58 4.2 The support count computation protocol 66 4.3 Privacy-preserving distributed Apriori protocol 72 5.1 Privacy preserving multi-party mean computation 79 5.2 Privacy preserving multi-party clustering protocol 81 5.3 Privacy preserving two-party clustering 86 5.4 Secure mean sharing 89 6.1 Private matrix product sharing (PMPS) 96 6.2 Protocol for two-party horizontally distributed data 98 6.3 Protocol for multi-party horizontally distributed data 101 6.4 Protocol for two-party vertically distributed data 103 vii Chapter INTRODUCTION 1.1 Privacy-preserving data mining: An overview Data mining plays an important role in the current world and provides us a powerful tool to efficiently discover valuable information from large databases [25] However, the process of mining data can result in a violation of privacy, therefore, issues of privacy preservation in data mining are receiving more and more attention from the this community [52] As a result, there are a large number of studies has been produced on the topic of privacy-preserving data mining (PPDM) [72] These studies deal with the problem of learning data mining models from the databases, while protecting data privacy at the level of individual records or the level of organizations Basically, there are three major problems in PPDM [8] First, the organizations such as government agencies wish to publish their data for researchers and even community However, they want to preserve the data privacy, for example, highly sensitive financial and health private data Second, a group of the organizations (or parties) wishes to together obtain the mining result on their joint data without disclosing each party’s privacy information Third, a miner wishes to collect data or obtain the data mining models from the individual users, while preserving privacy of each user Consequently, PPDM can be formed into three following areas depending on the models of information sharing Privacy-preserving data publishing: The model of this research consists of only an organization, is the trusted data holder This organization wishes to publish its data to the miner or the research community such that the anonymized data are useful for the data mining applications For example, some hospitals collect records from their patients for the some required SUMMARY This thesis have proposed four solutions for four problems in privacy preserving data mining in distributed data In each solution, we provided analysis to prove privacy and correctness based on the semi-honest security model and the secure multi-party computation methods We also evaluated the communication cost and computational complexity based on the estimation method In addition, we provided some experimental results to show how efficient and practical of the solutions In the first work, we proposed a solution for privacy-preserving data mining in a new scenario so-called 2PFD setting In this setting, each record is owned by two different users, one user only knows the values for a subset of attributes and the other knows the values for the remaining attributes We proposed a solution allows a miner to learn the frequency-based data mining models in 2PFD setting such as naive Bayes learning, decision tree learning, association rules mining, Pearson correlation analysis, etc., while preserving each users privacy The crucial step in the proposed solution is the privacy-preserving computation of frequencies of a tuple of values in the users data We illustrated the applicability of the solution by using it to build the privacy preserving protocol for the naive Bayes classifier learning Experimental results show that our protocol is efficient We also showed an improvement of technique using Shamir secure sharing scheme to allows the miner to be able to obtain frequency without requiring the full participating of all user pairs In the second work, we considered a scenario in which data are collected and maintained in vertically distributed model by different parties We proposed protocols allow the parties to cooperate for frequent itemset mining on their joint data, while preserving privacy of participants The important security feature of our protocols which are better than the previous protocols’s one in way that we achieve the full privacy protection of the parties That is, we not assume the existence of any kind of trusted parties Moreover, no collusion of parties can make any privacy breaches, unless all parties together 107 make a single collusion, which does not exist in fact In third work, we considered a data set that is horizontally partitioned into several parties We proposed a solution that allows the parties to cluster the joint data set using the EM algorithm, without revealing anything except for the final results So, each party could learn the cluster to which each of their data objects belongs, but they learn nothing else We gave two protocols for privacy preserving EM-based clustering: one for multi-party distributed data and one for two-party distributed data For the multi-party protocol, unlike the existing protocol, it does not reveal sum results of numerator and denominator in the secure computation for the parameters of EM algorithm, therefore, the proposed protocol is more secure and it allows the number of participating parties to be arbitrary For two-party case, we proposed a protocol that allows computing covariance matrices and final results without revealing the private information and the means In forth work, we have proposed a solution for privacy-preserving multivariate outlier detection on both vertically and horizontally distributed data models Basically, the proposed solution is based on techniques: linear transformation, private matrix product sharing, secure mean sharing and secure sum Privacy of the protocols in the solution is based on both Semi-honest and expansion security models In addition, we provided the experiments to show the computational complexity of the protocol is linear in the number of data attributes and the size of database We also show that they are very efficient in horizontally distributed data that mainly depends on the number of data attributes The proposed solution is useful in the scenario that multi parties wish to cooperate for outlier detection on their joint data sets, while they want to keep data privacy For example, two companies need to share their network log data to building an intrusion detection system, some banks need to share their customers data to find fraud cases, etc Also, although proposed solution are technically mature enough to be used for several applications of the privacy preserving data mining There are still some issues of solutions that are able to be improved in the future The general question for proposed solutions is that how could participants 108 may extend to the malicious behavior ? Although we can use some available solutions to integrate into our solutions for against malicious adversaries, the current solutions may be quite expensive for practical applications Thus, finding the efficient solutions may defend against malicious adversaries is an open problem Another interesting problem is to develop a general solution for privacy-preserving distributed data mining such that any privacy preserving data mining problem can be solved by using this solution Finding the general solution may be impossible, but the simple idea that we can design building blocks for primitive problems such as frequency mining, mean computation and so on, then these building blocks can be composed to design solutions for more complex problems, and make a general programming interface for future use Thus an open problem is to find the general primitives for the various data mining algorithms and design the building blocks for these primitives In addition it can ensure that the compositions of primitives has to meet the privacy purpose as well as the optimization in efficient for many data mining applications For 2PFD setting, in current the main problem we solved, is the privacy preserving frequency mining, which can be the key component of privacy preserving protocols for several data mining tasks such as naive Bayes learning, decision tree learning, association rules mining, Pearson correlation analysis, etc There may be many other tasks of privacy preserving data mining in 2PFD setting, such as multivariate regression analysis, that would be of interest for future work In addition, in the proposed frequency mining solution in 2PFD, half of the users need two interactions with the miner, so a natural question is whether we can design a method in which each user needs only one interaction with the miner For forth work, though the linear transformation technique meets the privacy model, ICA-based attacks method can cause the privacy breaches to the our transformation approach Thus, an open problem is to carefully select the transformation matrix to adapt for each particular data distribution such that the chosen perturbation is more resilient to the ICA-based attacks This problem should be investigated in the future 109 PUBLICATION LIST [1]“Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting”, IEICE Trans Information Systems, Vol.E93-D, No.10, 27012708, October 2010 [2] “Enhancing Privacy in Distributed Data Clustering”, Journal of Computer Science and Cybernetics, Vol 26, No 2, 1-15, 2010 [3] “Enhancing Privacy in Frequent Itemset Mining, submitted to journal “Expert Systems with Applications”, Elsevier [4] “Privacy Preserving Classification in Two-Dimension Distributed Data”, International Conference on Knowledge and Systems Engineering KSE 2010, 7-9 October, Hanoi, 96-103, 2010 [5] “Privacy preserving EM-based Clustering”, IEEE RIVF International Conference on Computing and Communication Technologies, RIVF09, 13-17 July 2009, 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NG DISTRIBUTED SOLUTIONS IN PRIVACY PRESERVING DATA MINING (Nghiên c u xây d ng m t s gi i pháp đ m b o an tồn thơng tin trình khai phá d li u) Chuyên ngành: B o đ m tốn h c cho máy tính h th... Furthermore, we can see that the 2PFD setting is quite popular in practice, and that privacy preserving frequency mining protocols in 2PFD are significant and can be applied to many other similar... executing the protocol, we generate three pairs of keys for each user, with the size of p and q set at 1024 bits and 160 bits, and compute values X and Y Note that generating these keys and parameters