A Practical Implementation of Secure Auctions Based on Multiparty Integer Computation

Bogetoft, Peter; Damgård, Ivan; Jakobsen, Thomas P.; Nielsen, Kurt; Pagter, Jakob; Toft, Tomas

doi:10.1007/11889663_10

Cited by 82 publications

(74 citation statements)

References 10 publications

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“…Until recently all work on secure multi-party computation has been essentially of a theoretical nature, focusing on feasibility results. However in the last few years a number of practical implementations have appeared [3,5,6,22,24].…”

Section: Introductionmentioning

confidence: 99%

Secure Two-Party Computation Is Practical

Pinkas

Schneider

Smart

et al. 2009

Lecture Notes in Computer Science

344

352

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Abstract. Secure multi-party computation has been considered by the cryptographic community for a number of years. Until recently it has been a purely theoretical area, with few implementations with which to test various ideas. This has led to a number of optimisations being proposed which are quite restricted in their application. In this paper we describe an implementation of the two-party case, using Yao's garbled circuits, and present various algorithmic protocol improvements. These optimisations are analysed both theoretically and empirically, using experiments of various adversarial situations. Our experimental data is provided for reasonably large circuits, including one which performs an AES encryption, a problem which we discuss in the context of various possible applications.

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Section: Introductionmentioning

confidence: 99%

Secure Two-Party Computation Is Practical

Pinkas

Schneider

Smart

et al. 2009

Lecture Notes in Computer Science

344

352

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“…For example, several solutions have been proposed that use SMC to enhance privacy in auctions [12], data clustering [13], [14] or filtering [15]. Recently, there is also an increased interest in combining SMC with methods of signal processing in order to be able to privately analyze signals; examples are the analysis of medical signals [16] or the evaluation of biometrics on encrypted data [2].…”

Section: Secure Multiparty Computation (Smc) Several Constructions Fmentioning

confidence: 99%

Secure computations on non-integer values

Franz

Deiseroth

Hamacher

et al. 2010

2010 IEEE International Workshop on Information Forensics and Security

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In this paper we present for the first time a framework that allows secure two-party computations on approximations of real valued signals. In our solution, we use a quantized logarithmic representation of the signal samples, which enables to represent both very small and very large numbers with bounded relative error. We show that numbers represented in this way can be encrypted using standard homomorphic encryption schemes; furthermore we give protocols that allow to perform all arithmetic operations on such encrypted values. Finally we demonstrate the practicality of our framework by applying it to the problem of filtering encrypted signals.

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“…The protocols in [6] and the recent result by [7] for constant-round SMC for any function requires at least three servers. All implementations of SMC [5,13] except ours use this model.…”

Section: Related Workmentioning

confidence: 99%

“…In [5] the model is applied to secure auctions which can be extended to benchmarking. Their implementation is based on variations of [6] and they report performance for three, five and seven servers.…”

Section: Related Workmentioning

confidence: 99%

“…Companies are reluctant to share their business performance data due to the risk of losing a competitive advantage or being embarrassed. Several privacy-preserving protocols that can be used for benchmarking that keep the KPIs confidential within one company have been proposed in the literature [2,5,9,10,13,20]. None of those matches the requirements of a large service provider offering a benchmarking service entirely.…”

Section: Introductionmentioning

confidence: 99%

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Practical Privacy-Preserving Benchmarking

Kerschbaum¹

IFIP – The International Federation for Information Processing

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Benchmarking is an important process for companies to stay competitive in today's markets. The basis for benchmarking are statistics of performance measures of a group of companies. The companies need to collaborate in order to compute these statistics.Protocols for privately computing statistics have been proposed in the literature. This paper designs, implements and evaluates a privacy-preserving benchmarking platform which is a central entity that offers a database of benchmark statistics to its customers. This is the first attempt at building a practical privacy-preserving benchmarking system and the first attempt at addressing all necessary trade-offs.The paper starts by designing a protocol that efficiently computes the statistics with constant cost per participant. The protocol uses central communication where customers only communicate with the central platform which facilitates a simple practical orchestration of the protocol. The protocols scale to realistic problem sizes due to the constant communication (and computation) cost per participant of the protocol.

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A Practical Implementation of Secure Auctions Based on Multiparty Integer Computation

Cited by 82 publications

References 10 publications

Secure Two-Party Computation Is Practical

Secure Two-Party Computation Is Practical

Secure computations on non-integer values

Practical Privacy-Preserving Benchmarking

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