Social-Aware Decentralization for Secure and Scalable Multi-party Computations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work studies the problem of MPC decentralization - that is, identifying a set of computing nodes to securely and efficiently execute the multi-party computation protocol (MPC) over a sensitive dataset. To balance between underdecentralization with high risk and over-decentralization with high cost, our unique approach is to add social-awareness, that is, the MPC protocol, running over a social network, is properly decentralized among the computing nodes selected carefully based on their social relationship. The key technical challenge is in estimating the risk of collusion between nodes on whom the computation is run. We propose solutions to estimate the risk of collusion based on (incomplete) social relationship, as well as algorithms for finding the MPC nodes such that the risk of collusion is minimized. We evaluate our methods on several real-world network datasets, and show that they are effective in minimizing the risk levels. This work has potential in enabling efficient privacy-preserving data sharing and computation in emerging big-data federation platforms, in healthcare, financial marketplaces, and other application domains.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages246-251
Number of pages6
ISBN (Electronic)9781538632925
DOIs
StatePublished - Jul 13 2017
Event37th IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2017 - Atlanta, United States
Duration: Jun 5 2017Jun 8 2017

Other

Other37th IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2017
CountryUnited States
CityAtlanta
Period6/5/176/8/17

Fingerprint

Data privacy
Costs
Big data

Keywords

  • Decentralization
  • graph algorithm
  • multi-party computation
  • protocol
  • social network

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems

Cite this

Tang, Y., & Soundarajan, S. (2017). Social-Aware Decentralization for Secure and Scalable Multi-party Computations. In Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017 (pp. 246-251). [7979824] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDCSW.2017.56

Social-Aware Decentralization for Secure and Scalable Multi-party Computations. / Tang, Yuzhe; Soundarajan, Sucheta.

Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 246-251 7979824.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tang, Y & Soundarajan, S 2017, Social-Aware Decentralization for Secure and Scalable Multi-party Computations. in Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017., 7979824, Institute of Electrical and Electronics Engineers Inc., pp. 246-251, 37th IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2017, Atlanta, United States, 6/5/17. https://doi.org/10.1109/ICDCSW.2017.56
Tang Y, Soundarajan S. Social-Aware Decentralization for Secure and Scalable Multi-party Computations. In Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 246-251. 7979824 https://doi.org/10.1109/ICDCSW.2017.56
Tang, Yuzhe ; Soundarajan, Sucheta. / Social-Aware Decentralization for Secure and Scalable Multi-party Computations. Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 246-251
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