Computing Maximum and Minimum with Privacy Preservation and Flexible Access Control

Wenxiu Ding, Zheng Yan, X.R. Qian, R.H. Deng

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

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    Abstract

    With the fast development of Internet of Things, huge volume of data is being collected from various sensors and devices, aggregated at gateways, and processed in the cloud. Due to privacy concern, data are usually encrypted before being outsourced to the cloud. However, encryption seriously impedes both computation over the data and sharing of the computation results. Computing maximum and minimum among a data set are two of the most basic operations in machine learning and data mining algorithms. In this paper, we study how to compute maximum and minimum over encrypted data and control the access to the computation result in a privacy-preserving manner. We present four schemes to realize privacy-preserving maximum and minimum computations with flexible access control that can adapt to various application scenarios. We further analyze their security and show their efficiency through extensive evaluations and comparisons with existing work. © 2019 IEEE.
    Original languageEnglish
    Title of host publicationIEEE Global Communications Conference
    PublisherIEEE
    ISBN (Electronic)9781728109626
    DOIs
    Publication statusPublished - 2019
    MoE publication typeA4 Conference publication
    EventIEEE Global Communications Conference - Waikoloa, United States
    Duration: 9 Dec 201913 Dec 2019

    Publication series

    NameIEEE Global Communications Conference
    ISSN (Print)2334-0983

    Conference

    ConferenceIEEE Global Communications Conference
    Abbreviated titleGLOBECOM
    Country/TerritoryUnited States
    CityWaikoloa
    Period09/12/201913/12/2019

    Keywords

    • access control
    • attribute-based encryption
    • homomorphic encryption
    • maximum and minimum
    • privacy preservation

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