Affiliations: [a] Department of Electrical and Computer Engineering, Cleveland State University, Cleveland, OH, USA | [b] Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA
Correspondence:
[*]
Corresponding author: Ye Zhu, Department of Electrical and Computer Engineering, Cleveland State University, Cleveland, OH 44115, USA. Tel.: +1 216 875 9749; Fax: +1 216 687 5405; E-mail: y.zhu61@csuohio.edu.
Note: [1] A preliminary version of the paper appeared in the 2008 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008).
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.
Keywords: Privacy, time series data mining, blind source separation
