Global-Scale Secure Multiparty Computation

Wang, Xiao; Ranellucci, Samuel; Katz, Jonathan

doi:10.1145/3133956.3133979

Cited by 133 publications

(91 citation statements)

References 20 publications

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“…Finally, we introduce our triple generation protocol, in two phases. Similarly to [WRK17b], we first show how to compute the cross terms in multiplication triples by computing so-called 'half-authenticated' triples. This protocol does not authenticate all terms and the result may yield an incorrect triple.…”

Section: Technical Overviewmentioning

confidence: 99%

“…We discuss this in more detail in Section 8. Our protocol starts to concretely improve upon previous protocols when we reach n = 30 parties and t = 18 corruptions: here, our triple generation method requires less than half the communication cost of the fastest MPC protocol which is also based on TinyOT [WRK17b] (dubbed WRK) tolerating up to n − 1 corruptions. For a fairer comparison, we also consider modifying WRK to run in a committee of size t + 1, to give a protocol with the same corruption threshold as ours.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, secure multi-party computation with a larger number of parties and a dishonest majority is far more difficult due to scalability challenges regarding the number of parties. Here, the most efficient practical protocol with active security has a multiplicative factor of O(λ/ log |C|) due to cut-and-choose [WRK17b] (where λ is a statistical security parameter and |C| is the size of the computed circuit). On the practical side, the same Boolean circuit of 30,000 gates can be securely evaluated at best in 500ms for 14 parties [WRK17b] in a local network where the latency is neglected, or in more than 20s in a wide network.…”

Section: Introductionmentioning

confidence: 99%

“…Here, the most efficient practical protocol with active security has a multiplicative factor of O(λ/ log |C|) due to cut-and-choose [WRK17b] (where λ is a statistical security parameter and |C| is the size of the computed circuit). On the practical side, the same Boolean circuit of 30,000 gates can be securely evaluated at best in 500ms for 14 parties [WRK17b] in a local network where the latency is neglected, or in more than 20s in a wide network. The problem is that current MPC protocols do not scale well with the number of parties, where the main bottleneck is a relatively high communication complexity, while the number of applications requiring large scale communication networks are constantly increasing, involving sometimes hundreds of parties.…”

Section: Introductionmentioning

confidence: 99%

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Concretely Efficient Large-Scale MPC with Active Security (or, TinyKeys for TinyOT)

Hazay

Orsini

Schöll

et al. 2018

Lecture Notes in Computer Science

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In this work we develop a new theory for concretely efficient, largescale MPC with active security. Current practical techniques are mostly in the strong setting of all-but-one corruptions, which leads to protocols that scale badly with the number of parties. To work around this issue, we consider a large-scale scenario where a small minority out of many parties is honest and design scalable, more efficient MPC protocols for this setting. Our results are achieved by introducing new techniques for information-theoretic MACs with short keys and extending the work of Hazay et al. (CRYPTO 2018), which developed new passively secure MPC protocols in the same context. We further demonstrate the usefulness of this theory in practice by analyzing the concrete communication overhead of our protocols, which improve upon the most efficient previous works.

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Section: Technical Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Concretely Efficient Large-Scale MPC with Active Security (or, TinyKeys for TinyOT)

Hazay

Orsini

Schöll

et al. 2018

Lecture Notes in Computer Science

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“…Given the wide variety of statistics that researchers would like to compute, in order to address this problem, better tools for compiling general statistical algorithms into MPC protocols need to be developed. In recent years, however, significant progress has been made in MPC compilers (Aly et al n.d.;Zhang et al 2013;Wang et al 2017). Although these compilers can be used to run arbitrary computations, the cryptographic requirements of the MPC protocols mean that the most efficient (insecure) algorithms for calculating a statistic of interest, may not be the most efficient algorithms to run within a secure computation (Esperança et al 2017).…”

Section: Statisticsmentioning

confidence: 99%

Computing Statistics from Private Data

Alter

Falk

et al. 2018

Data Science Journal

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In several domains, privacy presents a significant obstacle to scientific and analytic research, and limits the economic, social, health and scholastic benefits that could be derived from such research. These concerns stem from the need for privacy about personally identifiable information (PII), commercial intellectual property, and other types of information. For example, businesses, researchers, and policymakers may benefit by analyzing aggregate information about markets, but individual companies may not be willing to reveal information about risks, strategies, and weaknesses that could be exploited by competitors. Extracting valuable utility from the new "big data" economy demands new privacy technologies to overcome barriers that impede sensitive data from being aggregated and analyzed.Secure multiparty computation (MPC) is a collection of cryptographic technologies that can be used to effectively cope with some of these obstacles, and provide a new means of allowing researchers to coordinate and analyze sensitive data collections, obviating the need for dataowners to share the underlying data sets with other researchers or with each other. This paper outlines the findings that were made during interdisciplinary workshops that examined potential applications of MPC to data in the social and health sciences.The primary goals of this work are to describe the computational needs of these disciplines and to develop a specific roadmap for selecting efficient algorithms and protocols that can be used as a starting point for interdisciplinary projects between cryptographers and data scientists.

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Secure Outsourcing of Cryptographic Circuits Manufacturing

et al. 2018

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The fabrication process of integrated circuits (ICs) is complex and requires the use of offshore foundries to lower the costs and to have access to leading-edge manufacturing facilities. Such an outsourcing trend leaves the possibility of inserting malicious circuitry (a.k.a. hardware Trojans) during the fabrication process, causing serious security concerns. Hardware Trojans are very hard and expensive to detect and can disrupt the entire circuit or covertly leak sensitive information via a subliminal channel. In this paper, we propose a formal model for assessing the security of ICs whose fabrication has been outsourced to an untrusted offshore manufacturer. Our model captures that the IC specification and design are trusted but the fabrication facility(ies) may be malicious. Our objective is to investigate security in an ideal sense and follows a simulation based approach that ensures that Trojans cannot release any sensitive information to the outside. It follows that the Trojans' impact in the overall IC operation, in case they exist, will be negligible up to simulation. We then establish that such level of security is in fact achievable for the case of a single and of multiple outsourcing facilities. We present two compilers for ICs for the single outsourcing facility case relying on verifiable computation (VC) schemes, and another two compilers for the multiple outsourcing facilities case, one relying on multi-server VC schemes, and the other relying on secure multiparty computation (MPC) protocols with certain suitable properties that are attainable by existing schemes.

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Global-Scale Secure Multiparty Computation

Cited by 133 publications

References 20 publications

Concretely Efficient Large-Scale MPC with Active Security (or, TinyKeys for TinyOT)

Concretely Efficient Large-Scale MPC with Active Security (or, TinyKeys for TinyOT)

Computing Statistics from Private Data

Secure Outsourcing of Cryptographic Circuits Manufacturing

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