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Balancing Privacy Risk and Benefit in Service Selection for Multiprovision Cloud Service Composition

Author

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  • Linyuan Liu

    (Department of E-Commerce, Nanjing Audit University, Nanjing 211815, China)

  • Haibin Zhu

    (Collaborative Systems Laboratory, Nipissing University, North Bay, ON P1B 8L7, Canada)

  • Shenglei Chen

    (Department of E-Commerce, Nanjing Audit University, Nanjing 211815, China)

Abstract

The popularity of cloud computing has fueled the growth in multiprovision cloud service composition (MPCSC), where each cloud service provider (CSP) can fulfill multiple tasks, i.e., offer multiple services, simultaneously. In the MPCSC, users would rather disclose some private data for more benefits (e.g., personalized services). However, the more private data is released, the more serious the privacy risk faced by users. In particular, the multiservice provision characteristic of MPCSC further exacerbates the privacy risk. Therefore, how to balance the privacy risk and benefit in service selection for MPCSC is a challenging research problem. In this paper, firstly we explore the service selection problem of balancing privacy risk and benefit in MPCSC (SSBM), then we propose an improved Kuhn–Munkres (KM) algorithm solution to the SSBM problem. Furthermore, we conduct a series of simulation experiments to evaluate the proposed approach. The experimental results show that the proposed approach is both efficient and effective for solving the SSBM problem.

Suggested Citation

  • Linyuan Liu & Haibin Zhu & Shenglei Chen, 2022. "Balancing Privacy Risk and Benefit in Service Selection for Multiprovision Cloud Service Composition," Mathematics, MDPI, vol. 10(10), pages 1-33, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1675-:d:815092
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    References listed on IDEAS

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