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From Collaborative to Privacy Preserving Sequential Pattern Mining

Abstract : Research in the areas of privacy-preserving techniques in databases and subsequently in privacy enhancement technologies has witnessed an explosive growth spurt in recent years. This escalation has been fueled primarily by the growing mistrust of individuals toward organizations collecting and disbursing their personally identifiable information (PII). Digital repositories have become increasingly susceptible to intentional or unintentional abuse, resulting in organizations to be liable under the privacy legislations that are increasingly being adopted by governments the world over. These privacy concerns have necessitated new advancements in the field of distributed data mining, wherein collaborating parties may be legally bound not to reveal the private information of their customers. In this chapter, first we present the sequential pattern discovery problem in a collaborative framework and subsequently enhance the architecture by introducing the context of privacy. Thus we propose to extract sequential patterns from distributed databases while preserving privacy. A salient feature of the proposal is its flexibility and as a result is more pertinent to mining operations for real-world applications in terms of efficiency and functionality. Furthermore, under some reasonable assumptions, we prove that the architecture and protocol employed by our algorithm for multi-party computation is secure. Finally, we conclude with some trends of current research being conducted in the field.
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Vishal Kapoor, Pascal Poncelet, François Trousset, Maguelonne Teisseire. From Collaborative to Privacy Preserving Sequential Pattern Mining. Privacy and Anonymity in Information Management Systems, pp.135-156, 2010, 978-1-84996-237-7. ⟨10.1007/978-1-84996-238-4_7⟩. ⟨lirmm-00798706⟩



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