Maturity and Performance of Programmable Secure Computation

Archer, David W.; Bogdanov, Dan; Pinkas, Benny; Pullonen, Pille

doi:10.1109/msp.2016.97

Cited by 29 publications

(26 citation statements)

References 41 publications

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“…The steady increase in computing power coupled with considerable algorithmic improvements have led to many realworld tests of MPC over the past few years. See (Archer et al 2016) for a survey of the computational efficiency of modern MPC protocols.…”

Section: Previous Applications Of Mpcmentioning

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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Section: Previous Applications Of Mpcmentioning

confidence: 99%

Computing Statistics from Private Data

Alter

Falk

et al. 2018

Data Science Journal

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“…This is a situation in which all modern frameworks perform rather well (i.e., seconds to minutes) [4].…”

Section: Scalabilitymentioning

confidence: 99%

User-centric distributed solutions for privacy-preserving analytics

2017

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“…A key hurdle facing wide adoption of MPC is the computational inefficiencies associated with evaluating arbitrary functions over sizable inputs. Consequently, an important body of recent research on MPC has been concerned with the development of optimizations targeting specific algorithms or settings [3]. In this paper, we do so for a very important (and we believe very large) class of problems that involve the evaluation of "network distance" algorithms over an interconnection of private networks.…”

Section: Secure Multi-party Computationmentioning

confidence: 99%

“…However, performing operations under MPC can be prohibitively costly when compared to executing the same operations over the inputs "in the open". This added cost comes from the communication overhead and the increased complexity of analyzing data obliviously [3].…”

Section: Scenario and Approachmentioning

confidence: 99%