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232
Privacy Preserving Data Mining
 JOURNAL OF CRYPTOLOGY
, 2000
"... In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated b ..."
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Cited by 525 (9 self)
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In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated by the need to both protect privileged information and enable its use for research or other purposes. The
Fairplay — a secure twoparty computation system
 In USENIX Security Symposium
, 2004
"... Advances in modern cryptography coupled with rapid growth in processing and communication speeds make secure twoparty computation a realistic paradigm. Yet, thus far, interest in this paradigm has remained mostly theoretical. This paper introduces Fairplay [28], a fullfledged system that implements ..."
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Cited by 229 (6 self)
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Advances in modern cryptography coupled with rapid growth in processing and communication speeds make secure twoparty computation a realistic paradigm. Yet, thus far, interest in this paradigm has remained mostly theoretical. This paper introduces Fairplay [28], a fullfledged system that implements generic secure function evaluation (SFE). Fairplay comprises a high level procedural definition language called SFDL tailored to the SFE paradigm; a compiler of SFDL into a onepass Boolean circuit presented in a language called SHDL; and Bob/Alice programs that evaluate the SHDL circuit in the manner suggested by Yao in [39]. This system enables us to present the first evaluation of an overall SFE in real settings, as well as examining its components and identifying potential bottlenecks. It provides a testbed of ideas and enhancements concerning SFE, whether by replacing parts of it, or by integrating with it. We exemplify its utility by examining several alternative implementations of oblivious transfer within the system, and reporting on their effect on overall performance. 1
Privacypreserving set operations
 in Advances in Cryptology  CRYPTO 2005, LNCS
, 2005
"... In many important applications, a collection of mutually distrustful parties must perform private computation over multisets. Each party’s input to the function is his private input multiset. In order to protect these private sets, the players perform privacypreserving computation; that is, no part ..."
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Cited by 161 (0 self)
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In many important applications, a collection of mutually distrustful parties must perform private computation over multisets. Each party’s input to the function is his private input multiset. In order to protect these private sets, the players perform privacypreserving computation; that is, no party learns more information about other parties ’ private input sets than what can be deduced from the result. In this paper, we propose efficient techniques for privacypreserving operations on multisets. By employing the mathematical properties of polynomials, we build a framework of efficient, secure, and composable multiset operations: the union, intersection, and element reduction operations. We apply these techniques to a wide range of practical problems, achieving more efficient results than those of previous work.
Protecting Data Privacy in Private Information Retrieval Schemes
 JCSS
"... Private Information Retrieval (PIR) schemes allow a user to retrieve the ith bit of an nbit data string x, replicated in k 2 databases (in the informationtheoretic setting) or in k 1 databases (in the computational setting), while keeping the value of i private. The main cost measure for suc ..."
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Cited by 133 (21 self)
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Private Information Retrieval (PIR) schemes allow a user to retrieve the ith bit of an nbit data string x, replicated in k 2 databases (in the informationtheoretic setting) or in k 1 databases (in the computational setting), while keeping the value of i private. The main cost measure for such a scheme is its communication complexity.
Priced Oblivious Transfer: How to Sell Digital Goods
 In Birgit Pfitzmann, editor, Advances in Cryptology — EUROCRYPT 2001, volume 2045 of Lecture Notes in Computer Science
, 2001
"... Abstract. We consider the question of protecting the privacy of customers buying digital goods. More specifically, our goal is to allow a buyer to purchase digital goods from a vendor without letting the vendor learn what, and to the extent possible also when and how much, it is buying. We propose s ..."
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Cited by 125 (7 self)
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Abstract. We consider the question of protecting the privacy of customers buying digital goods. More specifically, our goal is to allow a buyer to purchase digital goods from a vendor without letting the vendor learn what, and to the extent possible also when and how much, it is buying. We propose solutions which allow the buyer, after making an initial deposit, to engage in an unlimited number of priced oblivioustransfer protocols, satisfying the following requirements: As long as the buyer’s balance contains sufficient funds, it will successfully retrieve the selected item and its balance will be debited by the item’s price. However, the buyer should be unable to retrieve an item whose cost exceeds its remaining balance. The vendor should learn nothing except what must inevitably be learned, namely, the amount of interaction and the initial deposit amount (which imply upper bounds on the quantity and total price of all information obtained by the buyer). In particular, the vendor should be unable to learn what the buyer’s current balance is or when it actually runs out of its funds. The technical tools we develop, in the process of solving this problem, seem to be of independent interest. In particular, we present the first oneround (twopass) protocol for oblivious transfer that does not rely on the random oracle model (a very similar protocol was independently proposed by Naor and Pinkas [21]). This protocol is a special case of a more general “conditional disclosure ” methodology, which extends a previous approach from [11] and adapts it to the 2party setting. 1
Secure multiparty computation of approximations
, 2001
"... Approximation algorithms can sometimes provide efficient solutions when no efficient exact computation is known. In particular, approximations are often useful in a distributed setting where the inputs are held by different parties and may be extremely large. Furthermore, for some applications, the ..."
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Cited by 108 (25 self)
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Approximation algorithms can sometimes provide efficient solutions when no efficient exact computation is known. In particular, approximations are often useful in a distributed setting where the inputs are held by different parties and may be extremely large. Furthermore, for some applications, the parties want to compute a function of their inputs securely, without revealing more information than necessary. In this work we study the question of simultaneously addressing the above efficiency and security concerns via what we call secure approximations. We start by extending standard definitions of secure (exact) computation to the setting of secure approximations. Our definitions guarantee that no additional information is revealed by the approximation beyond what follows from the output of the function being approximated. We then study the complexity of specific secure approximation problems. In particular, we obtain a sublinearcommunication protocol for securely approximating the Hamming distance and a polynomialtime protocol for securely approximating the permanent and related #Phard problems. 1
Random projectionbased multiplicative data perturbation for privacy preserving distributed data mining
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2006
"... This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matri ..."
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Cited by 94 (6 self)
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This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other datamining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores Independent Component Analysis as a possible tool for breaching privacy in deterministic multiplicative perturbationbased models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projectionbased technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacypreserving data mining applications.
Cryptographic Techniques for PrivacyPreserving Data Mining
 SIGKDD Explorations
, 2002
"... Research in secure distributed computation, which was done as part of a larger body of research in the theory of cryptography, has achieved remarkable results. It was shown that nontrusting parties can jointly compute functions of their different inputs while ensuring that no party learns anything ..."
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Cited by 92 (0 self)
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Research in secure distributed computation, which was done as part of a larger body of research in the theory of cryptography, has achieved remarkable results. It was shown that nontrusting parties can jointly compute functions of their different inputs while ensuring that no party learns anything but the defined output of the function. These results were shown using generic constructions that can be applied to any function that has an ecient representation as a circuit. We describe these results, discuss their efficiency, and demonstrate their relevance to privacy preserving computation of data mining algorithms. We also show examples of secure computation of data mining algorithms that use these generic constructions.