Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Time series compressibility and privacy
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Towards the evaluation of time series protection methods
Information Sciences: an International Journal
Efficient Detection of Discords for Time Series Stream
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Privacy-preserving discovery of frequent patterns in time series
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
On privacy in time series data mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Scalable secure multiparty computation
CRYPTO'06 Proceedings of the 26th annual international conference on Advances in Cryptology
IEEE Transactions on Fuzzy Systems
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Privacy preserving data publishing is one of the most important issues of privacy preserving data mining, but the problem of privately publishing time series data has not received enough attention. Random perturbation is an efficient method of privately publishing data. Random noise addition introduces uncertainty into published data, increasing the difficult of conjecturing the original values. The existing Gaussian white noise addition distributes the same amount of noise to every single attribute of each series, incurring the great decrease of data utility for classification purpose. Through analyzing the different impact of local regions on overall classification pattern, we formally define the concept of discord region which strongly influences the classification performance. We perturb original series differentially according to their position, whether in a discord region, to improve classification utility of published data. The experimental results on real and synthetic data verify the effectiveness of our proposed methods.