FAANST: fast anonymizing algorithm for numerical streaming data
DPM'10/SETOP'10 Proceedings of the 5th international Workshop on data privacy management, and 3rd international conference on Autonomous spontaneous security
Disclosure detection over data streams in database publishing
Proceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop
Fast clustering-based anonymization approaches with time constraints for data streams
Knowledge-Based Systems
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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 customers’ privacy. In this paper, we consider the problem of preserving customer’s 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.