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512
A PrivacyPreserving Index for Range Queries
, 2004
"... Database outsourcing is an emerging data management paradigm which has the potential to transform the IT operations of corporations. ..."
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Cited by 123 (9 self)
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Database outsourcing is an emerging data management paradigm which has the potential to transform the IT operations of corporations.
Secure MultiParty Computation Problems and Their Applications: A Review And Open Problems
 In New Security Paradigms Workshop
, 2001
"... The growth of the Internet has triggered tremendous opportunities for cooperative computation, where people are jointly conducting computation tasks based on the private inputs they each supplies. These computations could occur between mutually untrusted parties, or even between competitors. For exa ..."
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Cited by 108 (1 self)
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The growth of the Internet has triggered tremendous opportunities for cooperative computation, where people are jointly conducting computation tasks based on the private inputs they each supplies. These computations could occur between mutually untrusted parties, or even between competitors. For example, customers might send to a remote database queries that contain private information; two competing financial organizations might jointly invest in a project that must satisfy both organizations' private and valuable constraints, and so on. Today, to conduct such computations, one entity must usually know the inputs from all the participants; however if nobody can be trusted enough to know all the inputs, privacy will become a primary concern. This problem is referred to as Secure Multiparty Computation Problem (SMC) in the literature. Research in the SMC area has been focusing on only a limited set of specific SMC problems, while privacy concerned cooperative computations call for SMC studies in a variety of computation domains. Before we can study the problems, we need to identify and define the specific SMC problems for those computation domains. We have developed a frame to facilitate this problemdiscovery task. Based on our framework, we have identified and defined a number of new SMC problems for a spectrum of computation domains. Those problems include privacypreserving database query, privacypreserving scientific computations, privacypreserving intrusion detection, privacypreserving statistical analysis, privacypreserving geometric computations, and privacypreserving data mining. The goal of this paper is not only to present our results, but also to serve as a guideline so other people can identify useful SMC problems in their own computation domains.
Improved Garbled Circuit: Free XOR Gates and Applications
"... Abstract. We present a new garbled circuit construction for twoparty secure function evaluation (SFE). In our oneround protocol, XOR gates are evaluated “for free”, which results in the corresponding improvement over the best garbled circuit implementations (e.g. Fairplay [19]). We build permutati ..."
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Cited by 108 (17 self)
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Abstract. We present a new garbled circuit construction for twoparty secure function evaluation (SFE). In our oneround protocol, XOR gates are evaluated “for free”, which results in the corresponding improvement over the best garbled circuit implementations (e.g. Fairplay [19]). We build permutation networks [26] and Universal Circuits (UC) [25] almost exclusively of XOR gates; this results in a factor of up to 4 improvement (in both computation and communication) of their SFE. We also improve integer addition and equality testing by factor of up to 2. We rely on the Random Oracle (RO) assumption. Our constructions are proven secure in the semihonest model. 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 107 (26 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.
Extending Oblivious Transfers Efficiently
, 2003
"... We consider the problem of extending oblivious transfers: Given a small number of oblivious transfers \for free," can one implement a large number of oblivious transfers? Beaver has shown how to extend oblivious transfers given a oneway function. However, this protocol is inecient in pract ..."
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Cited by 94 (1 self)
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We consider the problem of extending oblivious transfers: Given a small number of oblivious transfers \for free," can one implement a large number of oblivious transfers? Beaver has shown how to extend oblivious transfers given a oneway function. However, this protocol is inecient in practice, in part due to its nonblackbox use of the underlying oneway function.
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.
Secure Multiparty Computation for PrivacyPreserving Data Mining
, 2008
"... In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacypreserving data mining. In addition to reviewing definitions and constructions for secure multiparty computation, we discuss the issue of efficiency and demon ..."
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Cited by 90 (0 self)
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In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacypreserving data mining. In addition to reviewing definitions and constructions for secure multiparty computation, we discuss the issue of efficiency and demonstrate the difficulties involved in constructing highly efficient protocols. We also present common errors that are prevalent in the literature when secure multiparty computation techniques are applied to privacypreserving data mining. Finally, we discuss the relationship between secure multiparty computation and privacypreserving data mining, and show which problems it solves and which problems it does not. 1
PrivacyPreserving Multivariate Statistical Analysis: Linear Regression and Classification
 In Proceedings of the 4th SIAM International Conference on Data Mining
, 2004
"... analysis technique that has found applications in various areas. In this paper, we study some multivariate statistical analysis methods in Secure 2party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical ana ..."
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Cited by 87 (1 self)
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analysis technique that has found applications in various areas. In this paper, we study some multivariate statistical analysis methods in Secure 2party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current statistical analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2party multivariate statistical analysis problems: Secure 2party Multivariate Linear Regression problem and Secure 2party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems.
Airavat: Security and Privacy for MapReduce
, 2009
"... The cloud computing paradigm, which involves distributed computation on multiple largescale datasets, will become successful only if it ensures privacy, confidentiality, and integrity for the data belonging to individuals and organizations. We present Airavat, a novel integration of decentralized i ..."
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Cited by 76 (4 self)
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The cloud computing paradigm, which involves distributed computation on multiple largescale datasets, will become successful only if it ensures privacy, confidentiality, and integrity for the data belonging to individuals and organizations. We present Airavat, a novel integration of decentralized information flow control (DIFC) and differential privacy that provides strong security and privacy guarantees for MapReduce computations. Airavat allows users to use arbitrary mappers, prevents unauthorized leakage of sensitive data during the computation, and supports automatic declassification of the results when the latter do not violate individual privacy. Airavat minimizes the amount of trusted code in the system and allows users without security expertise to perform privacypreserving computations on sensitive data. Our prototype implementation demonstrates the flexibility of Airavat on a wide variety of case studies. The prototype is efficient, with runtimes on Amazon’s cloud computing infrastructure within 25 % of a MapReduce system with no security.