document: On the tradeoff between privacy and utility in data publishing In data publishing , anonymization techniques such as generalization and bucketization have been designed to provide privacy protection . In the meanwhile , they reduce the utility of the data . It is important to consider the tradeoff between privacy and utility . In a paper that appeared in KDD 2008 , Brickell and Shmatikov proposed an evaluation methodology by comparing privacy gain with utility gain resulted from anonymizing the data , and concluded that `` even modest privacy gains require almost complete destruction of the data-mining utility '' . This conclusion seems to undermine existing work on data anonymization . In this paper , we analyze the fundamental characteristics of privacy and utility , and show that it is inappropriate to directly compare privacy with utility . We then observe that the privacy-utility tradeoff in data publishing is similar to the risk-return tradeoff in financial investment , and propose an integrated framework for considering privacy-utility tradeoff , borrowing concepts from the Modern Portfolio Theory for financial investment . Finally , we evaluate our methodology on the Adult dataset from the UCI machine learning repository . Our results clarify several common misconceptions about data utility and provide data publishers useful guidelines on choosing the right tradeoff between privacy and utility .
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