Satisfying Privacy Requirements Before Data Anonymization

In this paper, we study a problem of protecting privacy of individuals in large public survey rating data. We propose a novel (k,ϵ, l)-anonymity model to protect privacy in large survey rating data, in which each survey record is required to be similar to at least k−1 other records based on the non-sensitive ratings, where the similarity is controlled by ϵ, and the standard deviation of sensitive ratings is at least l. We study an interesting yet non-trivial satisfaction problem of the proposed model, which is to decide whether a survey rating data set satisfies the privacy requirements given by the user. For this problem, we investigate its inherent properties theoretically, and devise a novel slicing technique to solve it. We analyze the computation complexity of the proposed slicing technique and conduct extensive experiments on two real-life data sets, and the results show that the slicing technique is fast and scalable with data size and much more efficient in terms of execution time and space overhead than the heuristic pairwise method.