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通讯作者：
Ye Zhu;Yongjian Fu;Huirong Fu
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作者：
Ye Zhu;Yongjian Fu;Huirong Fu
.传统的数据挖掘隐私保护研究主要集中在时不变隐私问题上。随着时间序列数据挖掘的出现，传统的基于快照的隐私问题需要扩展到多维，增加了时间维度。我们发现，目前的技术，以保护隐私的数据挖掘是不能有效地保护时域隐私。提出了基于统计信号处理中盲源分离技术的时间序列数据挖掘中的数据流分离攻击。我们用真实的数据进行的实验表明，这种攻击是有效的。通过结合数据流分离方法和频率匹配方法，攻击者可以识别数据源并危及时域隐私。本文针对数据流分离攻击提出了可能的对策。
. 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.