On the identity anonymization of high‐dimensional rating data
Xiaoxun Sun, Hua Wang, Yanchun Zhang · Concurrency and Computation Practice and Experience · 2011
SUMMARY We study the challenges of protecting the privacy of individuals in a large public survey rating data. The survey rating data usually contains both ratings of sensitive and non‐sensitive issues. The ratings of sensitive issues involve personal privacy. Although the survey participants do not reveal any of their ratings, their survey records are potentially identifiable by using information from other public sources. None of the existing anonymization principles (e.g.k‐anonymity,l‐diversity, etc.) can effectively prevent such breaches in large survey rating data sets. In this paper, we tackle the problem by defining a principle called (k, epsilon, l)‐anonymity. The principle requires that, for each transactiontin the given survey rating dataT, at least (k− 1) other transactions inTmust have ratings similar tot, where the similarity is controlled by ε and the standard deviation of sensitive ratings is at leastl. We propose a greedy approach to anonymize the survey rating data that scales almost linearly with the input size, and we apply the method to two real‐life data sets to demonstrate their efficiency and practical utility. Copyright © 2011 John Wiley & Sons, Ltd.