Document Type
Article
Publication Date
2010
Publication Title
Intelligent Data Analysis
Abstract
Traditional research on preserving privacy in data mining focuses on time-invariant privacy issues. With the emergence of time series data mining, traditional snapshot-based privacy issues need to be extended to be multi-dimensional with the addition of time dimension. We find current techniques to preserve privacy in data mining are not effective in preserving time-domain privacy. We present the data flow separation attack on privacy in time series data mining, which is based on blind source separation techniques from statistical signal processing. Our experiments with real data show that this attack is effective. By combining the data flow separation method and the frequency matching method, an attacker can identify data sources and compromise time-domain privacy. We propose possible countermeasures to the data flow separation attack in the paper.
Repository Citation
Zhu, Ye; Fu, Yongjian; and Fu, Huirong, "A New Class of Attacks on Time Series Data Mining" (2010). Electrical and Computer Engineering Faculty Publications. 41.
https://engagedscholarship.csuohio.edu/enece_facpub/41
Original Citation
Y. Zhu, Y. Fu and H. Fu, "A new class of attacks on time series data mining\m{1}," Intelligent Data Analysis, vol. 14, pp. 405-418, 06, 2010.
DOI
10.3233/IDA-2010-0428
Version
Postprint
Publisher's Statement
© 2010 – IOS Press and the authors. All rights reserved
Volume
14
Issue
3
