Toward secure clustered multi-party computation: A privacy-preserving clustering protocol

Sedigheh Abbasi, Stelvio Cimato, Ernesto Damiani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Despite a large amount of research work has been done and a large number of results produced, the deployment of Secure Multi-party Computation (SMC) protocols for solving practical problems in real world scenarios is still an issue. This is mainly due to the complexity of the SMC-based solutions and to the needed assumptions that are not easy to fit to the considered problem. In this paper we propose an innovative approach for the deployment of SMC, providing a tradeoff between efficiency and privacy. In the Secure Clustered Multi-Party Computation (SCMC) approach, a function is more efficiently computed through reducing the number of participants to the SMC protocol by clustering, such that a reasonable privacy leakage inside the cluster is allowed. Toward this direction, this paper verifies the impact and the feasibility of applying different clustering techniques over the participants to a SMC protocol and proposes an effective specifically-tailored clustering protocol.

Original languageBritish English
Title of host publicationInformation and Communication Technology - International Conference, ICT-EurAsia 2013, Proceedings
Pages447-452
Number of pages6
DOIs
StatePublished - 2013
EventInternational Conference on Information and Communication Technology, ICT-EurAsia 2013 - Yogyakarta, Indonesia
Duration: 25 Mar 201329 Mar 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7804 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Information and Communication Technology, ICT-EurAsia 2013
Country/TerritoryIndonesia
CityYogyakarta
Period25/03/1329/03/13

Keywords

  • Privacy and Efficiency Tradeoff
  • Privacy-Preserving Clustering
  • Secure Multi-Party Computation

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