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Privacy Preserving Sequential Pattern Mining in Distributed Databases

Abstract : Research in the areas of privacy preserving techniques in databases and subsequently in privacy enhancement technologies have witnessed an explosive growth-spurt in recent years. This escalation has been fueled by the growing mistrust of individuals towards 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 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 paper, we present a new algorithm PriPSeP (Privacy Preserving SEquential Patterns) for the mining of sequential patterns from distributed databases while preserving privacy. A salient feature of PriPSeP is that due to its flexibility it is more pertinent to mining operations for real world applications in terms of efficiency and functionality. Under some reasonable assumptions, we prove that our architecture and protocol employed by our algorithm for multi-party computation is secure.
keyword : Datamining Privacy
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Submitted on : Friday, September 20, 2019 - 12:05:48 PM
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Vishal Kapoor, Pascal Poncelet, François Trousset, Maguelonne Teisseire. Privacy Preserving Sequential Pattern Mining in Distributed Databases. CIKM: Conference on Information and Knowledge Management, Nov 2006, Arlington, Virginia, United States. pp.758-767, ⟨10.1145/1183614.1183722⟩. ⟨lirmm-00135021⟩



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