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

Linyuan Liu, Haibin Zhu and Shenglei Chen
Additional contact information
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

Mathematics, 2022, vol. 10, issue 10, 1-33

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.

Keywords: cloud computing; data security; privacy risk; personalized services; service selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/10/1675/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/10/1675/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:10:p:1675-:d:815092

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().