Validating Privacy Requirements in Large Survey Rating Data

Recent study shows that supposedly anonymous movie rating records are de-identified by using a little auxiliary information. In this chapter, we study a problem of protecting privacy of individuals in large public survey rating data. Such rating data usually contains both ratings of sensitive and non-sensitive issues, and the ratings of sensitive issues belong to personal privacy. Even when survey participants do not reveal any of their ratings, their survey records are potentially identifiable by using information from other public sources. To amend this, in this chapter, we propose a novel (k;e; l)-anonymity model to protect privacy in large survey rating data, in which each survey record is required to be 'similar' with at least k−1 others based on the non-sensitive ratings, where the similarity is controlled by e, 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 slice\udtechnique to solve it. We discuss the idea of how to anonymize data by using the result of satisfaction problem. Finally, we 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