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

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• 作者：Gaofeng Zhang (20)
  Xuyun Zhang (21)
  Yun Yang (20) (22)
  Chang Liu (21)
  Jinjun Chen (21)
  
• 关键词：Cloud service ; Privacy protection ; Noise generation ; Association probability
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7636
• 期：1
• 页码：648-655
• 全文大小：370KB
• 参考文献：1. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility. Future Generation Computer Systems?25(6), 599-16 (2009) CrossRef
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  10. Liu, K., Jin, H., Chen, J., Liu, X., Yuan, D., Yang, Y.: A Compromised-time-cost Scheduling Algorithm in SwinDeW-C for Instance-intensive Cost-constrained Workflows on a Cloud Computing Platform. International Journal of High Performance Computing Applications?24(4), 445-56 (2010) CrossRef
  
• 作者单位：Gaofeng Zhang (20)
  Xuyun Zhang (21)
  Yun Yang (20) (22)
  Chang Liu (21)
  Jinjun Chen (21)
  
  20. Faculty of Information and Communication Technologies, Swinburne University of Technology, Hawthorn, Melbourne, Australia, 3122
  21. Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway, NSW, Australia, 2007
  22. School of Computer Science and Technology, Anhui University, Hefei, 230039, China
  
• ISSN：1611-3349

文摘

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.