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Satisfying Privacy Requirements Before Data Anonymization, Xiaoxun Sun, H Wang, J Li, Y Zhang
Satisfying Privacy Requirements Before Data Anonymization, Xiaoxun Sun, H Wang, J Li, Y Zhang
Xiaoxun Sun
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 …
Injecting Purpose And Trust Into Data Anonymisation, Xiaoxun Sun, H Wang, J Li, Y Zhang
Injecting Purpose And Trust Into Data Anonymisation, Xiaoxun Sun, H Wang, J Li, Y Zhang
Xiaoxun Sun
Data anonymisation is of increasing importance for allowing sharing individual data among various data requesters for a variety of social network data analysis and mining applications. Most existing works of data anonymisation target at the optimization of the anonymisation metrics to balance the data utility and privacy, whereas they ignore the effects of a requester’s trust level and application purposes during the data anonymisation. Our aim of this paper is to propose a much finer level anonymisation scheme with regard to the data requester’s trust and specific application purpose. We firstly prioritize the attributes for anonymisation based on their importance …
On The Identity Anonymization Of High-Dimensional Rating Data, Xiaoxun Sun, H Wang, Y Zhang
On The Identity Anonymization Of High-Dimensional Rating Data, Xiaoxun Sun, H Wang, Y Zhang
Xiaoxun Sun
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 …