Preserving Privacy in Time Series Data Mining
Document Type
Article
Publication Date
2011
Publication Title
International Journal of Data Warehousing and Mining
Abstract
Time series data mining poses new challenges to privacy. Through extensive experiments, the authors find that existing privacy-preserving techniques such as aggregation and adding random noise are insufficient due to privacy attacks such as data flow separation attack. This paper also presents a general model for publishing and mining time series data and its privacy issues. Based on the model, a spectrum of privacy preserving methods is proposed. For each method, effects on classification accuracy, aggregation error, and privacy leak are studied. Experiments are conducted to evaluate the performance of the methods. The results show that the methods can effectively preserve privacy without losing much classification accuracy and within a specified limit of aggregation error.
Repository Citation
Zhu, Ye; Fu, Yongjian; and Fu, Huirong, "Preserving Privacy in Time Series Data Mining" (2011). Electrical and Computer Engineering Faculty Publications. 216.
https://engagedscholarship.csuohio.edu/enece_facpub/216
Original Citation
H. Fu, Y. Fu and Y. Zhu, "Preserving privacy in time series data mining," International Journal of Data Warehousing and Mining, vol. 7, pp. 64+, October; 2014/3, 2011.
DOI
10.4018/jdwm.2011100104
Volume
7
Issue
4
Comments
The work was supported in part by the US National Science Foundation under Grant No. 1144644 and the Junior Faculty Mini-Grant Program from the Maxine Goodman Levin College of Urban Affairs of Cleveland State University.