HHUIF and MSICF: Novel algorithms for privacy preserving utility mining
Expert Systems with Applications: An International Journal
From itemsets through trajectories to location based services: a knowledge hiding privacy approach
ACM SIGKDD Explorations Newsletter
Revisiting sequential pattern hiding to enhance utility
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Utility-preserving transaction data anonymization with low information loss
Expert Systems with Applications: An International Journal
Utility-guided Clustering-based Transaction Data Anonymization
Transactions on Data Privacy
Privacy protection in personalized web search: a peer group-based approach
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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Sensitive knowledge hiding in large transactional databases is one of the major goals of privacy preserving data mining. However, it is only recently that researchers were able to identify exact solutions for the hiding of knowledge, depicted in the form of sensitive frequent itemsets and their related association rules. Exact solutions allow for the hiding of vulnerable knowledge without any critical compromises, such as the hiding of nonsensitive patterns or the accidental uncovering of infrequent itemsets, amongst the frequent ones, in the sanitized outcome. In this paper, we highlight the process of border revision, which plays a significant role towards the identification of exact hiding solutions, and we provide efficient algorithms for the computation of the revised borders. Furthermore, we review two algorithms that identify exact hiding solutions, and we extend the functionality of one of them to effectively identify exact solutions for a wider range of problems (than its original counterpart). Following that, we introduce a novel framework for decomposition and parallel solving of hiding problems, which are handled by each of these approaches. This framework improves to a substantial degree the size of the problems that both algorithms can handle and significantly decreases their runtime. Through experimentation, we demonstrate the effectiveness of these approaches toward providing high quality knowledge hiding solutions.