Showing papers by "Benny Pinkas published in 2008"

FairplayMP: a system for secure multi-party computation

Abstract: We present FairplayMP (for "Fairplay Multi-Party"), a system for secure multi-party computation. Secure computation is one of the great achievements of modern cryptography, enabling a set of untrusting parties to compute any function of their private inputs while revealing nothing but the result of the function. In a sense, FairplayMP lets the parties run a joint computation that emulates a trusted party which receives the inputs from the parties, computes the function, and privately informs the parties of their outputs. FairplayMP operates by receiving a high-level language description of a function and a configuration file describing the participating parties. The system compiles the function into a description as a Boolean circuit, and perform a distributed evaluation of the circuit while revealing nothing else. FairplayMP supplements the Fairplay system [16], which supported secure computation between two parties. The underlying protocol of FairplayMP is the Beaver-Micali-Rogaway (BMR) protocol which runs in a constant number of communication rounds (eight rounds in our implementation). We modified the BMR protocol in a novel way and considerably improved its performance by using the Ben-Or-Goldwasser-Wigderson (BGW) protocol for the purpose of constructing gate tables. We chose to use this protocol since we believe that the number of communication rounds is a major factor on the overall performance of the protocol. We conducted different experiments which measure the effect of different parameters on the performance of the system and demonstrate its scalability. (We can now tell, for example, that running a second-price auction between four bidders, using five computation players, takes about 8 seconds.)

Implementing Two-Party Computation Efficiently with Security Against Malicious Adversaries

Abstract: We present an implementation of the protocol of Lindell and Pinkas for secure two-party computation which is secure against malicious adversaries [13]. This is the first running system which provides security against malicious adversaries according to rigorous security definition and without using the random oracle model. We ran experiments showing that the protocol is practical. In addition we show that there is little benefit in replacing subcomponents secure in the standard model with those which are only secure in the random oracle model. Throughout we pay particular attention to using the most efficient subcomponents in the protocol, and we select parameters for the encryption schemes, commitments and oblivious transfers which are consistent with a security level equivalent to AES-128.

Secure Multiparty Computation for Privacy-Preserving Data Mining.

Abstract: In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacy-preserving 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 privacy-preserving data mining. Finally, we discuss the relationship between secure multiparty computation and privacy-preserving data mining, and show which problems it solves and which problems it does not.

An Efficient Protocol for Secure Two-Party Computation in the Presence of Malicious Adversaries.

Abstract: We show an efficient secure two-party protocol, based on Yao’s construction, which provides security against malicious adversaries. Yao’s original protocol is only secure in the presence of semi-honest adversaries, and can be transformed into a protocol that achieves security against malicious adversaries by applying the compiler of Goldreich, Micali and Wigderson (the “GMW compiler”). However, this approach does not seem to be very practical as it requires using generic zero-knowledge proofs. Our construction is based on applying cut-and-choose techniques to the original circuit and inputs. Security is proved according to the ideal/real simulation paradigm, and the proof is in the standard model (with no random oracle model or common reference string assumptions). The resulting protocol is computationally efficient: the only usage of asymmetric cryptography is for running O(1) oblivious transfers for each input bit (or for each bit of a statistical security parameter, whichever is larger). Our protocol combines techniques from folklore (like cut-andchoose) along with new techniques for efficiently proving consistency of inputs. We remark that a naive implementation of the cut-and-choose technique with Yao’s protocol does not yield a secure protocol. This is the first paper to show how to properly implement these techniques, and to provide a full proof of security. Our protocol can also be interpreted as a constant-round black-box reduction of secure two-party computation to oblivious transfer and perfectly-hiding commitments, or a black-box reduction of secure two-party computation to oblivious transfer alone, with a number of rounds which is linear in a statistical security parameter. These two reductions are comparable to Kilian’s reduction, which uses OT alone but incurs a number of rounds which is linear in the depth of the circuit [19].

Peer-to-Peer Secure Multi-party Numerical Computation

Abstract: We propose an efficient framework for enabling secure multi-party numerical computations in a Peer-to-Peer network. This problem arises in a range of applications such as collaborative filtering, distributed computation of trust and reputation, monitoring and numerous other tasks, where the computing nodes would like to preserve the privacy of their inputs while performing a joint computation of a certain function. Although there is a rich literature in the field of distributed systems security concerning secure multi-party computation, in practice it is hard to deploy those methods in very large scale Peer-to-Peer networks. In this work, we examine several possible approaches and discuss their feasibility. Among the possible approaches, we identify a single approach which is both scalable and theoretically secure. An additional novel contribution is that we show how to compute the neighborhood based collaborative filtering, a state-of-the-art collaborative filtering algorithm, winner of the Netflix progress prize of the year 2007. Our solution computes this algorithm in a Peer-to-Peer network, using a privacy preserving computation, without loss of accuracy. Using extensive large scale simulations on top of real Internet topologies, we demonstrate the applicability of our approach. As far as we know, we are the first to implement such a large scale secure multi-party simulation of networks of millions of nodes and hundreds of millions of edges.

Peer-to-Peer Secure Multi-Party Numerical Computation

Abstract: We propose an efficient framework for enabling secure multi-party numerical computations in a Peer-to-Peer network. This problem arises in a range of applications such as collaborative filtering, distributed computation of trust and reputation, monitoring and numerous other tasks, where the computing nodes would like to preserve the privacy of their inputs while performing a joint computation of a certain function. Although there is a rich literature in the field of distributed systems security concerning secure multi-party computation, in practice it is hard to deploy those methods in very large scale Peer-to-Peer networks. In this work, we examine several possible approaches and discuss their feasibility. Among the possible approaches, we identify a single approach which is both scalable and theoretically secure. An additional novel contribution is that we show how to compute the neighborhood based collaborative filtering, a state-of-the-art collaborative filtering algorithm, winner of the Netflix progress prize of the year 2007. Our solution computes this algorithm in a Peer-to-Peer network, using a privacy preserving computation, without loss of accuracy. Using extensive large scale simulations on top of real Internet topologies, we demonstrate the applicability of our approach. As far as we know, we are the first to implement such a large scale secure multi-party simulation of networks of millions of nodes and hundreds of millions of edges.