Computationally Private Information Retrieval with Polylogarithmic Communication

Cachin, Christian; Micali, Silvio; Stadler, M.

doi:10.1007/3-540-48910-x_28

Cited by 454 publications

(453 citation statements)

References 16 publications

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“…The following theorem relates the complexity of privately computing a functionality g to the circuit size of g. Theorem 2.1 ([54,27,3]). Let C = {C n } be a uniform family of (deterministic or probabilistic 7 8 A similar statement holds also when the adversary is active [39,34,49].…”

Section: Secure Multiparty Computationmentioning

confidence: 82%

“…The receiver outputs the bit x i , and the sender has no output. if stronger cryptographic assumptions are made, in this case specific number-theoretic assumptions from [7,41,22]. We refer the reader to the above papers for a more concrete analysis of the complexity tradeoffs and to [21] for a general survey of PIR protocols.…”

Section: Definition 2 (Oblivious Transfer)mentioning

confidence: 99%

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Private Multiparty Sampling and Approximation of Vector Combinations

Ishai

Malkin

Strauss

et al. 2007

Automata, Languages and Programming

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a b s t r a c tWe consider the problem of private efficient data mining of vertically-partitioned databases. Each of several parties holds a column of a data matrix (a vector) and the parties want to investigate the componentwise combination of their vectors. The parties want to minimize communication and local computation while guaranteeing privacy in the sense that no party learns more than necessary. Sublinear-communication private protocols have primarily been studied only in the two-party case. In contrast, this work focuses on multiparty settings.First, we give efficient private multiparty protocols for sampling a row of the data matrix and for computing arbitrary functions of a random row, where the row index is additively shared among two or more parties. These results can be used to obtain private approximation protocols for several useful combination functionalities. Moreover, these results have some interesting consequences for the general problem of reducing sublinear-communication secure multiparty computation to two-party private information retrieval (PIR).Second, we give protocols for computing approximations (summaries) of the componentwise sum, minimum, and maximum of the columns. Here, while providing a weaker privacy guarantee (where the approximation may leak up to the entire output vector), our protocols are extremely efficient. In particular, the required cryptographic overhead (compared to non-private solutions) is polylogarithmic in the number of rows.

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Section: Secure Multiparty Computationmentioning

confidence: 82%

Section: Definition 2 (Oblivious Transfer)mentioning

confidence: 99%

Private Multiparty Sampling and Approximation of Vector Combinations

Ishai

Malkin

Strauss

et al. 2007

Automata, Languages and Programming

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“…All known PIR protocols realization can be divided into two classes: single-server protocols (see [2][3][4][5][6][7][8][9][10]) and multi-servers ones (see [11][12][13][14]). …”

Section: Existing Schemesmentioning

confidence: 99%

Сomputationally efficient private information retrieval protocol

Afanasyeva¹,

Bezzateev²

2016

Naučno-teh. vestn. inf. tehnol. meh. opt.

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“…On the other hand, PIR schemes do not protect the owner of the database and do not prevent the user from learning more than a single element. The first constructions of PIR schemes were based on using separate databases which do not communicate, but more recent constructions [20,5] use only a single database. More recently attention was given to the question of protecting the database as well, i.e.…”

Section: Comparison To Private Information Retrieval (Pir)mentioning

confidence: 99%

“…For many applications the communication in the initialization phase is not an issue, and can be done using devices such as DVD's, jaz drives, or a fast communication network. In contrast, single server PIR protocols [20,5] are very costly to implement since each transfer requires O(N ) exponentiations (which must be done after the receiver sends her query).…”

Section: Comparison To Private Information Retrieval (Pir)mentioning

confidence: 99%

Oblivious Transfer with Adaptive Queries

Naor

Pinkas

1999

Lecture Notes in Computer Science

140

124

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Abstract. We provide protocols for the following two-party 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 − 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 on-line computation involves roughly log N invocations of a 1-out-2 OT protocol. The protocols are based on a new primitive, sum consistent synthesizers.

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Computationally Private Information Retrieval with Polylogarithmic Communication

Cited by 454 publications

References 16 publications

Private Multiparty Sampling and Approximation of Vector Combinations

Private Multiparty Sampling and Approximation of Vector Combinations

Сomputationally efficient private information retrieval protocol

Oblivious Transfer with Adaptive Queries

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