K-anonymity model for privacy-preserving soccer fitness data publishing

Abstract

With the development of data mining technology, more and more researchers use the soccer fitness data to analyse the ranking of soccer athletes' and professional training. However, the direct release of soccer fitness data may leak the personal privacy of soccer athletes, so how to ensure the utility of soccer fitness data and the privacy of soccer player has become an issue. In this paper, we point out the linking attack existing in soccer fitness data, which the attackers can use the auxiliary demographic data as background information to attack the published physical data. So the attackers will map the privacy data and the athlete together. At the same time, we apply the partitioning-based and k-means clustering-based two k-anonymity algorithms to the soccer fitness data publishing to trade-offs the data utility and the personal privacy. Experimental results showed that the performance of methods is convincing.