Privacy protection on sliding window of data streams

Abstract

In many applications, transaction data arrive in the form of high speed data streams. These data contain a lot of information about customers that needs to be carefully managed to protect customerspsila privacy. In this paper, we consider the problem of preserving customerpsilas privacy on the sliding window of transaction data streams. This problem is challenging because sliding window is updated frequently and rapidly. We propose a novel approach, SWAF (sliding window anonymization framework), to solve this problem by continuously facilitating k-anonymity on the sliding window. Three advantages make SWAF practical: (1) Small processing time for each tuple of data steam. (2) Small memory requirement. (3) Both privacy protection and utility of anonymized sliding window are carefully considered. Theoretical analysis and experimental results show that SWAF is efficient and effective.