Privacy FP-Tree

Abstract

Current technology has made the publication of people’s private information a common occurrence. The implications for individual privacy and security are still largely poorly understood by the general public but the risks are undeniable as evidenced by the increasing number of identity theft cases being reported recently. Two new definitions of privacy have been developed recently to help understand the exposure and how to protect individuals from privacy violations, namely, anonymized privacy and personalized privacy. This paper develops a methodology to validate whether a privacy violation exists for a published dataset. Determining whether privacy violations exist is a non-trivial task. Multiple privacy definitions and large datasets make exhaustive searches ineffective and computationally costly. We develop a compact tree structure called the Privacy FP-Tree to reduce the costs. This data structure stores the information of the published dataset in a format that allows for simple, efficient traversal. The Privacy FP-Tree can effectively determine the anonymity level of the dataset as well as identify any personalized privacy violations. This algorithm is O (n log n) , which has acceptable characteristics for this application. Finally, experiments demonstrate the approach is scalable and practical.