Anonymization of moving objects databases by clustering and perturbation
Information Systems
Preserving privacy in semantic-rich trajectories of human mobility
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
C-safety: a framework for the anonymization of semantic trajectories
Transactions on Data Privacy
Revisiting sequential pattern hiding to enhance utility
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Trajectory anonymity in publishing personal mobility data
ACM SIGKDD Explorations Newsletter
Knowledge hiding from tree and graph databases
Data & Knowledge Engineering
Utility-maximizing event stream suppression
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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The process of discovering relevant patterns holding in a database was first indicated as a threat to database security by O'Leary in [CHECK END OF SENTENCE]. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent item sets mining. Following many real-world data and application demands, in this paper, we shift the problem of knowledge hiding to contexts where both the data and the extracted knowledge have a sequential structure. We define the problem of hiding sequential patterns and show its NP-hardness. Thus, we devise heuristics and a polynomial sanitization algorithm. Starting from this framework, we specialize it to the more complex case of spatiotemporal patterns extracted from moving objects databases. Finally, we discuss a possible kind of attack to our model, which exploits the knowledge of the underlying road network, and enhance our model to protect from this kind of attack. An exhaustive experiential analysis on real-world data sets shows the effectiveness of our proposal.