Privacy preservation algorithm for service-oriented information search

Personalized information services offer a promising way to improve the accuracy of Web search, but they bring about additional requirements related to data privacy preservation. Nevertheless, current SOA usually have one of the main barriers for deploying personalized search applications, and how to do privacy-preserving personalization is a great challenge. Privacy becomes a more and more serious concern in service-oriented information search, so good algorithms are in need to be designed. In this paper, the authors considered the previous research on k-anonymity involving the influence of Quasi-identifier on sensitive attribute Bottom-up k-anonymity and present a method for calculating the influence of Quasi-identifier on sensitive attribute Bottom-up k-anonymity through microdata directly and improved utility matrix. To better evaluate the information loss of the anonymity data, the authors also present a quality metric, both the two major factors: data utility and privacy guarantee are well preserved, Improved Normalized Certainty Penalty (INCP). To achieve better privacy protection, the authors present a method based on the utility of Quasi-identifier which is L-diversity satisfied.