Time-series pattern based effective noise generation for privacy protection on cloud

Zhang, Gaofeng, Liu, Xiao and Yang, Yun 2015, Time-series pattern based effective noise generation for privacy protection on cloud, IEEE transactions on computers, vol. 64, no. 5, pp. 1456-1469, doi: 10.1109/TC.2014.2298013.

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Title Time-series pattern based effective noise generation for privacy protection on cloud
Author(s) Zhang, Gaofeng
Liu, XiaoORCID iD for Liu, Xiao orcid.org/0000-0001-8400-5754
Yang, Yun
Journal name IEEE transactions on computers
Volume number 64
Issue number 5
Start page 1456
End page 1469
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-05
ISSN 0018-9340
Keyword(s) Science & Technology
Technology
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Computer Science
Engineering
Cloud computing
Privacy protection
Noise obfuscation
Noise generation
Time-series pattern
Cluster
SYSTEMS
Summary Cloud computing is proposed as an open and promising computing paradigm where customers can deploy and utilize IT services in a pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness and virtualization, various malicious service providers may exist in these cloud environments, and some of them may record service data from a customer and then collectively deduce the customer's private information without permission. Therefore, from the perspective of cloud customers, it is essential to take certain technical actions to protect their privacy at client side. Noise obfuscation is an effective approach in this regard by utilizing noise data. For instance, noise service requests can be generated and injected into real customer service requests so that malicious service providers would not be able to distinguish which requests are real ones if these requests' occurrence probabilities are about the same, and consequently related customer privacy can be protected. Currently, existing representative noise generation strategies have not considered possible fluctuations of occurrence probabilities. In this case, the probability fluctuation could not be concealed by existing noise generation strategies, and it is a serious risk for the customer's privacy. To address this probability fluctuation privacy risk, we systematically develop a novel time-series pattern based noise generation strategy for privacy protection on cloud. First, we analyze this privacy risk and present a novel cluster based algorithm to generate time intervals dynamically. Then, based on these time intervals, we investigate corresponding probability fluctuations and propose a novel time-series pattern based forecasting algorithm. Lastly, based on the forecasting algorithm, our novel noise generation strategy can be presented to withstand the probability fluctuation privacy risk. The simulation evaluation demonstrates that our strategy can significantly improve the effectiveness of such cloud privacy protection to withstand the probability fluctuation privacy risk.
Language eng
DOI 10.1109/TC.2014.2298013
Field of Research 080303 Computer System Security
080501 Distributed and Grid Systems
0803 Computer Software
0805 Distributed Computing
1006 Computer Hardware
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082909

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