Privacy preserving sequential pattern mining in distributed databases
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Privacy preserving data mining of sequential patterns for network traffic data
Information Sciences: an International Journal
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
A lower bound on the sample size needed to perform a significant frequent pattern mining task
Pattern Recognition Letters
Sequence Data Mining
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In this demonstration, we aim to present the ACSM prototype that deals with the discovery of frequent patterns in the context of flow management problems. One important issue while working on such problems is to ensure the preservation of private data collected from the users. The approach presented here is based on the representation of flows in the form of probabilistic automata. Resorting to efficient algebraic techniques, the ACSM prototype is able to discover from those automata sequential patterns under constraints. Contrary to standard sequential pattern techniques that may be applied in such contexts, our prototype makes no use of individuals data.