Practically Efficient Multi-party Sorting Protocols from Comparison Sort Algorithms

Hamada, Keisuke; Kikuchi, Ryo; Ikarashi, Dai; Chida, Koji; Takahashi, Katsumi

doi:10.1007/978-3-642-37682-5_15

Cited by 58 publications

(33 citation statements)

References 26 publications

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“…-Secure Sorting: Secure Sorting using MPC can be achieved by sorting networks and other data-oblivious mechanisms, including the randomize shellsort from Goodrich [16]. Moreover, Hamada et al [17] introduced a technique to facilitate the use of comparison sorting algorithms. This technique consists of randomly permuting the vector before sorting, so that the results of some of the intermediate secure comparisons can be made public.…”

Section: Preliminariesmentioning

confidence: 99%

An MPC-Based Privacy-Preserving Protocol for a Local Electricity Trading Market

Abidin

Aly

Cleemput

et al. 2016

Cryptology and Network Security

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Abstract. This paper proposes a decentralised and privacy-preserving local electricity trading market. The market employs a bidding protocol based on secure multiparty computation and allows users to trade their excess electricity among themselves. The bid selection and trading price calculation are performed in a decentralised and privacy-preserving manner. We implemented the market in C++ and tested its performance with realistic data sets. Our simulation results show that the market tasks can be performed for 2500 bids in less than four minutes in the "online" phase, showing its feasibility for a typical electricity trading period.

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Section: Preliminariesmentioning

confidence: 99%

An MPC-Based Privacy-Preserving Protocol for a Local Electricity Trading Market

Abidin

Aly

Cleemput

et al. 2016

Cryptology and Network Security

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“…Some provide secure integer arithmetic [46,14,32,27,20,3,42], floating point arithmetic [17,26], shuffling and sorting [29] and linking [25], but, to our knowledge, only the Sharemind framework provides all the operations integrated into a single implementation [10,44,43,38,8].…”

Section: Secure Multi-party Computation With External Partiesmentioning

confidence: 99%

Rmind: A Tool for Cryptographically Secure Statistical Analysis

Bogdanov

Kamm

Laur

et al. 2018

IEEE Trans. Dependable and Secure Comput.

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Abstract. Secure multi-party computation platforms are becoming more and more practical. This has paved the way for privacy-preserving statistical analysis using secure multi-party computation. Simple statistical analysis functions have been emerging here and there in literature, but no comprehensive system has been compiled. We describe and implement the most used statistical analysis functions in the privacy-preserving setting including simple statistics, t-test, χ 2 test, Wilcoxon tests and linear regression. We give descriptions of the privacy-preserving algorithms and benchmark results that show the feasibility of our solution.

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“…Calculating a Cox model requires first sorting the data, and then performing an iterative optimization algorithm. Secure sorting is itself a well-studied task (Jónsson et al 2011;Hamada et al 2013), and most secure sorting is based on sorting networks (Batcher 1968). Even after (securely) sorting the data, optimizing the objective function requires a relatively complex secure calculation, e.g.…”

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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Practically Efficient Multi-party Sorting Protocols from Comparison Sort Algorithms

Cited by 58 publications

References 26 publications

An MPC-Based Privacy-Preserving Protocol for a Local Electricity Trading Market

An MPC-Based Privacy-Preserving Protocol for a Local Electricity Trading Market

Rmind: A Tool for Cryptographically Secure Statistical Analysis

Computing Statistics from Private Data

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