Keywords: Unlike conventional methods that exert the same amount of privacy control over all the tuples in the microdata, personalized privacy preservation applies various degrees of protection to different tuples, subject to the preferences of the data owners. This chapter explains the formulation of personal preferences, and the computation of anonymized tables that fulfill the privacy requirement of everybody. Several theoretical results regarding privacy guarantees will also be discussed. Finally, we point out the open research issues that may be explored in the future. Personalized, k-anonymity, l-diversity. In earlier chapters, we have seen several principles, such as k-anonymity  and l-diversity , for determining the degree of privacy preservation in data publication. A common feature of those principles is that, they impose
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