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
A fast fixed-point algorithm for independent component analysis
Neural Computation
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
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Pattern Extraction for Time Series Classification
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Using randomized response techniques for privacy-preserving data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering time series from ARMA models with clipped data
Proceedings of the tenth 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
Optimal randomization for privacy preserving data mining
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
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Blind source separation using clustering-based multivariate densityestimation algorithm
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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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 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.