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413
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 372 (8 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
A modular approach to the design and analysis of authentication and key exchange protocols
, 1998
"... We present a general framework for constructing and analyzing authentication protocols in realistic models of communication networks. This framework provides a sound formalization for the authentication problem and suggests simple and attractive design principles for general authentication and key e ..."
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Cited by 222 (19 self)
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We present a general framework for constructing and analyzing authentication protocols in realistic models of communication networks. This framework provides a sound formalization for the authentication problem and suggests simple and attractive design principles for general authentication and key exchange protocols. The key element in our approach is a modular treatment of the authentication problem in cryptographic protocols; this applies to the definition of security, to the design of the protocols, and to their analysis. In particular, following this modular approach, we show how to systematically transform solutions that work in a model of idealized authenticated communications into solutions that are secure in the realistic setting of communication channels controlled by an active adversary. Using these principles we construct and prove the security of simple and practical authentication and keyexchange protocols. In particular, we provide a security analysis of some wellknown key exchange protocols (e.g. authenticated DiffieHellman key exchange), and of some of the techniques underlying the design of several authentication protocols that are currently being
Privacy Preserving Association Rule Mining in Vertically Partitioned Data
 In The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2002
"... Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some attributes of each transaction, and the sites wish to collaborate to identify globally valid association ru ..."
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Cited by 207 (20 self)
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Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some attributes of each transaction, and the sites wish to collaborate to identify globally valid association rules. However, the sites must not reveal individual transaction data. We present a twoparty algorithm for efficiently discovering frequent itemsets with minimum support levels, without either site revealing individual transaction values.
Revealing information while preserving privacy
 In PODS
, 2003
"... We examine the tradeoff between privacy and usability of statistical databases. We model a statistical database by an nbit string d1,.., dn, with a query being a subset q ⊆ [n] to be answered by � i∈q di. Our main result is a polynomial reconstruction algorithm of data from noisy (perturbed) subset ..."
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Cited by 199 (10 self)
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We examine the tradeoff between privacy and usability of statistical databases. We model a statistical database by an nbit string d1,.., dn, with a query being a subset q ⊆ [n] to be answered by � i∈q di. Our main result is a polynomial reconstruction algorithm of data from noisy (perturbed) subset sums. Applying this reconstruction algorithm to statistical databases we show that in order to achieve privacy one has to add perturbation of magnitude Ω ( √ n). That is, smaller perturbation always results in a strong violation of privacy. We show that this result is tight by exemplifying access algorithms for statistical databases that preserve privacy while adding perturbation of magnitude Õ(√n). For timeT bounded adversaries we demonstrate a privacypreserving access algorithm whose perturbation magnitude is ≈ √ T. 1
Limits on the Provable Consequences of Oneway Permutations
, 1989
"... We present strong evidence that the implication, "if oneway permutations exist, then secure secret key agreement is possible" is not provable by standard techniques. Since both sides of this implication are widely believed true in real life, to show that the implication is false requires a new m ..."
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Cited by 162 (0 self)
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We present strong evidence that the implication, "if oneway permutations exist, then secure secret key agreement is possible" is not provable by standard techniques. Since both sides of this implication are widely believed true in real life, to show that the implication is false requires a new model. We consider a world where dl parties have access to a black box or a randomly selected permutation. Being totally random, this permutation will be strongly oneway in provable, informationthevretic way. We show that, if P = NP, no protocol for secret key agreement is secure in such setting. Thus, to prove that a secret key greement protocol which uses a oneway permutation as a black box is secure is as hrd as proving F NP. We also obtain, as corollary, that there is an oracle relative to which the implication is false, i.e., there is a oneway permutation, yet secretexchange is impossible. Thus, no technique which relativizes can prove that secret exchange can be based on any oneway permutation. Our results present a general framework for proving statements of the form, "Cryptographic application X is not likely possible based solely on complexity assumption Y." 1
Universally Composable TwoParty and MultiParty Secure Computation
, 2002
"... We show how to securely realize any twoparty and multiparty functionality in a universally composable way, regardless of the number of corrupted participants. That is, we consider an asynchronous multiparty network with open communication and an adversary that can adaptively corrupt as many pa ..."
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Cited by 125 (32 self)
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We show how to securely realize any twoparty and multiparty functionality in a universally composable way, regardless of the number of corrupted participants. That is, we consider an asynchronous multiparty network with open communication and an adversary that can adaptively corrupt as many parties as it wishes. In this setting, our protocols allow any subset of the parties (with pairs of parties being a special case) to securely realize any desired functionality of their local inputs, and be guaranteed that security is preserved regardless of the activity in the rest of the network. This implies that security is preserved under concurrent composition of an unbounded number of protocol executions, it implies nonmalleability with respect to arbitrary protocols, and more. Our constructions are in the common reference string model and rely on standard intractability assumptions.
General Secure MultiParty Computation from any Linear SecretSharing Scheme
, 2000
"... Abstract. We show that verifiable secret sharing (VSS) and secure multiparty computation (MPC) among a set of n players can efficiently be based on any linear secret sharing scheme (LSSS) for the players, provided that the access structure of the LSSS allows MPC or VSS at all. Because an LSSS neith ..."
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Cited by 122 (20 self)
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Abstract. We show that verifiable secret sharing (VSS) and secure multiparty computation (MPC) among a set of n players can efficiently be based on any linear secret sharing scheme (LSSS) for the players, provided that the access structure of the LSSS allows MPC or VSS at all. Because an LSSS neither guarantees reconstructability when some shares are false, nor verifiability of a shared value, nor allows for the multiplication of shared values, an LSSS is an apparently much weaker primitive than VSS or MPC. Our approach to secure MPC is generic and applies to both the informationtheoretic and the cryptographic setting. The construction is based on 1) a formalization of the special multiplicative property of an LSSS that is needed to perform a multiplication on shared values, 2) an efficient generic construction to obtain from any LSSS a multiplicative LSSS for the same access structure, and 3) an efficient generic construction to build verifiability into every LSSS (always assuming that the adversary structure allows for MPC or VSS at all). The protocols are efficient. In contrast to all previous informationtheoretically secure protocols, the field size is not restricted (e.g, to be greater than n). Moreover, we exhibit adversary structures for which our protocols are polynomial in n while all previous approaches to MPC for nonthreshold adversaries provably have superpolynomial complexity. 1
Tools for Privacy Preserving Distributed Data Mining
 ACM SIGKDD Explorations
, 2003
"... Privacy preserving mining of distributed data has numerous applications. Each application poses di#erent constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the sol ..."
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Cited by 121 (7 self)
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Privacy preserving mining of distributed data has numerous applications. Each application poses di#erent constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be combined for specific privacypreserving data mining applications. This paper presents some components of such a toolkit, and shows how they can be used to solve several privacypreserving data mining problems.
Pseudonym Systems
, 1999
"... Pseudonym systems allow users to interact with multiple organizations anonymously, using pseudonyms. The pseudonyms cannot be linked, but are formed in such a way that a user can prove to one organization a statement about his relationship with another. Such statement is called a credential. Previou ..."
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Cited by 118 (11 self)
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Pseudonym systems allow users to interact with multiple organizations anonymously, using pseudonyms. The pseudonyms cannot be linked, but are formed in such a way that a user can prove to one organization a statement about his relationship with another. Such statement is called a credential. Previous work in this area did not protect the system against dishonest users who collectively use their pseudonyms and credentials, i.e. share an identity. Previous practical schemes also relied very heavily on the involvement of a trusted center. In the present paper we give a formal definition of pseudonym systems where users are motivated not to share their identity, and in which the trusted center's involvement is minimal. We give theoretical constructions for such systems based on any oneway function. We also suggest an efficient and easy to implement practical scheme. This is joint work with Ronald L. Rivest and Amit Sahai.
PrivacyPreserving KMeans Clustering over Vertically Partitioned Data
 IN SIGKDD
, 2003
"... Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for kmeans cl ..."
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Cited by 116 (7 self)
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Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for kmeans clustering when different sites contain different attributes for a common set of entities. Each site learns the cluster of each entity, but learns nothing about the attributes at other sites.