Data and Structural k-Anonymity in Social Networks
Privacy, Security, and Trust in KDD
Publishing naive Bayesian classifiers: privacy without accuracy loss
Proceedings of the VLDB Endowment
ACM Transactions on Database Systems (TODS)
Utility-maximizing event stream suppression
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Privacy preservation in data mining demands protecting both input and output privacy. The former refers to sanitizing the raw data itself before performing mining. The latter refers to preventing the mining output (model/pattern) from malicious pattern-based inference attacks. The preservation of input privacy does not necessarily lead to that of output privacy. This work studies the problem of protecting output privacy in the context of frequent pattern mining over data streams. After exposing the privacy breaches existing in current stream mining systems, we propose Butterfly, a light-weighted countermeasure that can effectively eliminate these breaches without explicitly detecting them, meanwhile minimizing the loss of the output accuracy. We further optimize the basic scheme by taking account of two types of semantic constraints, aiming at maximally preserving utility-related semantics while maintaining the hard privacy and accuracy guarantee. We conduct extensive experiments over real-life datasets to show the effectiveness and efficiency of our approach.