Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Optimizing time series discretization for knowledge discovery
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Fast window correlations over uncooperative time series
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Adaptive event detection with time-varying poisson processes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Preserving Privacy in Time Series Data Classification by Discretization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Measuring long-term location privacy in vehicular communication systems
Computer Communications
A review on time series data mining
Engineering Applications of Artificial Intelligence
Discord region based analysis to improve data utility of privately published time series
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Specialization in i* strategic rationale diagrams
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
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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 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.