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211
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 71 (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.
Communication Preserving Protocols for Secure Function Evaluation
 In Proc. of 33rd STOC
, 2001
"... A secure function evaluation protocol allows two parties to jointly compute a function f(x; y) of their inputs in a manner not leaking more information than necessary. A major result in this field is: "any function f that can be computed using polynomial resources can be computed securely usin ..."
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Cited by 63 (5 self)
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A secure function evaluation protocol allows two parties to jointly compute a function f(x; y) of their inputs in a manner not leaking more information than necessary. A major result in this field is: "any function f that can be computed using polynomial resources can be computed securely using polynomial resources" (where `resources' refers to communication and computation). This result follows by a general transformation from any circuit for f to a secure protocol that evaluates f . Although the resources used by protocols resulting from this transformation are polynomial in the circuit size, they are much higher (in general) than those required for an insecure computation of f . We propose a new methodology for designing secure protocols, utilizing the communication complexity tree (or branching program) representation of f . We start with an efficient (insecure) protocol for f and transform it into a secure protocol. In other words, "any function f that can be computed using communication complexity c can be can be computed securely using communication complexity that is polynomial in c and a security parameter". We show several simple applications of this new methodology resulting in protocols efficient either in communication or in computation. In particular, we exemplify a protocol for the "millionaires problem ", where two participants want to compare their values but reveal no other information. Our protocol is more efficient than previously known ones in either communication or computation. 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 61 (5 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.
Keyword search and oblivious pseudorandom functions
, 2005
"... We study the problem of privacypreserving access to a database. Particularly, we consider the problem of privacypreserving keyword search (KS), where records in the database are accessed according to their associated keywords and where we care for the privacy of both the client and the server. W ..."
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Cited by 51 (4 self)
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We study the problem of privacypreserving access to a database. Particularly, we consider the problem of privacypreserving keyword search (KS), where records in the database are accessed according to their associated keywords and where we care for the privacy of both the client and the server. We provide efficient solutions for various settings of KS, based either on specific assumptions or on general primitives (mainly oblivious transfer). Our general solutions rely on a new connection between KS and the oblivious evaluation of pseudorandom functions (OPRFs). We therefore study both the definition and construction of OPRFs and, as a corollary, give improved constructions of OPRFs that may be of independent interest.
Privacy Preserving Naive Bayes Classifier for Vertically Partitioned Data
 in Proceedings of the fourth SIAM Conference on Data Mining, 2004
"... PrivacyPreserving Data Mining – developing models without seeing the data – is receiving growing attention. This paper assumes a privacypreserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. Naïve Bayes is often ..."
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Cited by 51 (7 self)
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PrivacyPreserving Data Mining – developing models without seeing the data – is receiving growing attention. This paper assumes a privacypreserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. Naïve Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This paper brings privacypreservation to Naïve Bayes classification on vertically partitioned data.
Single Database Private Information Retrieval Implies Oblivious Transfer
, 2000
"... A SingleDatabase Private Information Retrieval (PIR) is a protocol that allows a user to privately retrieve from a database an entry with as small as possible communication complexity. We call a PIR protocol nontrivial if its total communication is strictly less than the size of the database. ..."
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Cited by 48 (6 self)
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A SingleDatabase Private Information Retrieval (PIR) is a protocol that allows a user to privately retrieve from a database an entry with as small as possible communication complexity. We call a PIR protocol nontrivial if its total communication is strictly less than the size of the database. Nontrivial PIR is an important cryptographic primitive with many applications. Thus, understanding which assumptions are necessary for implementing such a primitive is an important task, although (so far) not a wellunderstood one. In this paper we show that any nontrivial PIR implies Oblivious Transfer, a far better understood primitive. Our result not only significantly clarifies our understanding of any nontrivial PIR protocol, but also yields the following consequences:  Any nontrivial PIR is complete for all twoparty and multiparty secure computations.
Oblivious Transfer with Adaptive Queries
 Proc. CRYPTO, Springer LNCS
, 1999
"... . We provide protocols for the following twoparty problem: One party, the sender, has N values and the other party, the receiver, would like to learn k of them, deciding which ones in an adaptive manner (i.e. the ith value may depend on the first i \Gamma 1 values). The sender does not want the rec ..."
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Cited by 47 (2 self)
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. We provide protocols for the following twoparty problem: One party, the sender, has N values and the other party, the receiver, would like to learn k of them, deciding which ones in an adaptive manner (i.e. the ith value may depend on the first i \Gamma 1 values). The sender does not want the receiver to obtain more than k values. This is a variant of the well known Oblivious Transfer (OT) problem and has applications in protecting privacy in various settings. We present efficient protocols for the problem that require an O(N) computation in the preprocessing stage and fixed computation (independent of k) for each new value the receiver obtains. The online computation involves roughly log N invocations of a 1out2 OT protocol. The protocols are based on a new primitive, sum consistent synthesizers. 1 Introduction Oblivious Transfer (abbrev. OT) refers to several types of twoparty protocols where at the beginning of the protocol one party, the Sender (or sometimes Bob or B), has ...
Selective private function evaluation with applications to private statistics
 In Proceedings of Twentieth ACM Symposium on Principles of Distributed Computing (PODC
, 2001
"... Motivated by the application of private statistical analysis of large databases, we consider the problem of selective private function evaluation (SPFE). In this problem, a client interacts with one or more servers holding copies of a database z = zt,...,z, in order to compute f(z~t,...,z~,,,) , fo ..."
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Cited by 47 (10 self)
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Motivated by the application of private statistical analysis of large databases, we consider the problem of selective private function evaluation (SPFE). In this problem, a client interacts with one or more servers holding copies of a database z = zt,...,z, in order to compute f(z~t,...,z~,,,) , for some function f and indices i = it,...,i, ~ chosen by the client. Ideally, the client must learn nothing more about the database than f(zit,..., zi,,~), and the servers should learn nothing. Generic solutions for this problem, based on standard techniques for secure function evaluation, incur communication complexity that is at least linear in n, making them prohibitive for large databases even when f is relatively simple and m is small. We present various approaches for constructing sublinearcommunication $PFE protocols, both for the general problem and for special cases of interest. Our solutions not only offer sublinear communication complexity, but are also practical in many scenarios. 1.
Noisy Polynomial Interpolation and Noisy Chinese Remaindering
, 2000
"... Abstract. The noisy polynomial interpolation problem is a new intractability assumption introduced last year in oblivious polynomial evaluation. It also appeared independently in password identification schemes, due to its connection with secret sharing schemes based on Lagrange’s polynomial interpo ..."
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Cited by 41 (2 self)
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Abstract. The noisy polynomial interpolation problem is a new intractability assumption introduced last year in oblivious polynomial evaluation. It also appeared independently in password identification schemes, due to its connection with secret sharing schemes based on Lagrange’s polynomial interpolation. This paper presents new algorithms to solve the noisy polynomial interpolation problem. In particular, we prove a reduction from noisy polynomial interpolation to the lattice shortest vector problem, when the parameters satisfy a certain condition that we make explicit. Standard lattice reduction techniques appear to solve many instances of the problem. It follows that noisy polynomial interpolation is much easier than expected. We therefore suggest simple modifications to several cryptographic schemes recently proposed, in order to change the intractability assumption. We also discuss analogous methods for the related noisy Chinese remaindering problem arising from the wellknown analogy between polynomials and integers. 1
Polylogarithmic private approximations and efficient matching
, 2005
"... In [12] a private approximation of a function f is defined to be another function F that approximates f in the usual sense, but does not reveal any information about x other than what can be deduced from f(x). We give the first twoparty private approximation of the l2 distance with polylogarithmi ..."
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Cited by 40 (3 self)
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In [12] a private approximation of a function f is defined to be another function F that approximates f in the usual sense, but does not reveal any information about x other than what can be deduced from f(x). We give the first twoparty private approximation of the l2 distance with polylogarithmic communication. This, in particular, resolves the main open question of [12]. We then look at the private near neighbor problem in which Alice has a query point in {0, 1} d and Bob a set of n points in {0, 1} d, and Alice should privately learn the point closest to her query. We improve upon existing protocols, resolving open questions of [13, 10]. Then, we relax the problem by defining the private approximate near neighbor problem, which requires introducing a notion of secure computation of approximations for functions that return sets of points rather than values. For this problem we give several protocols with sublinear communication.