Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Hiding sensitive patterns from sequence databases: research challenges and solutions

Loukides, Grigorios and Gkoulalas-Divanis, Aris 2013. Hiding sensitive patterns from sequence databases: research challenges and solutions. Presented at: 2013 IEEE 14th International Conference on Mobile Data Management (MDM), Milan, Italy, 3-6 June 2013. Published in: Bettini, C. and Wolfson, O. eds. Proceedings of the 2013 IEEE 14th International Conference on Mobile Data Management (MDM). Los Alamitos, CA: IEEE, pp. 45-50. 10.1109/MDM.2013.64

Full text not available from this repository.

Abstract

Sequence data are encountered in a plethora of applications, spanning from telecommunications to web usage analysis, marketing and healthcare. Disseminating these data offers remarkable opportunities for discovering interesting patterns, but it is challenging to perform in a privacy-preserving way. Although there is a large gamut of techniques to anonymizing sequential data, the discovery of sensitive sequential patterns through data mining algorithms may still lead to serious privacy violations. This is because the mining of such patterns enables intrusive inferences about the habits of a portion of the population, or provides the means for unsolicited advertisement and user profiling. In this paper, we present the problem of hiding sensitive sequential patterns, and survey existing works that attempt to address it. In addition, we discuss the important research challenges that pertain to solving this problem, and present a roadmap for future work.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IEEE
ISBN: 9781467360685
Last Modified: 12 Jun 2019 02:52
URI: https://orca.cardiff.ac.uk/id/eprint/59436

Citation Data

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item