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An Association Probability Based Noise Generation Strategy for Privacy Protection in Cloud Computing

  • Gaofeng Zhang
  • Xuyun Zhang
  • Yun Yang
  • Chang Liu
  • Jinjun Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7636)

Abstract

Cloud computing allows customers to utilise IT services in a pay-as-you-go fashion to save huge cost on IT infrastructure. In open cloud, ‘malicious’ service providers could record service data from a cloud customer and collectively deduce the customer’s privacy without the customer’s permission. Accordingly, customers need to take certain actions to protect their privacy automatically at client sides, such as noise obfuscation. For instance, it can generate and inject noise service requests into real ones so that service providers are hard to distinguish which ones are real. Existing noise obfuscations focus on concealing occurrence probabilities of service requests. But in reality, association probabilities of service requests can also reveal customer privacy. So, we present a novel association probability based noise generation strategy by concealing these association probabilities. The simulation comparison demonstrates that this strategy can improve the effectiveness of privacy protection significantly from the perspective of association probability.

Keywords

Cloud service Privacy protection Noise generation Association probability 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gaofeng Zhang
    • 1
  • Xuyun Zhang
    • 2
  • Yun Yang
    • 3
    • 1
  • Chang Liu
    • 2
  • Jinjun Chen
    • 2
  1. 1.Faculty of Information and Communication TechnologiesSwinburne University of TechnologyMelbourneAustralia
  2. 2.Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia
  3. 3.School of Computer Science and TechnologyAnhui UniversityHefeiChina

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