Two Privacy-Preserving Approaches for Publishing Transactional Data Streams

Abstract

Recently, data mining over transactional data streams has become an attractive research area. However, releasing raw transactional data streams, in which only explicit identifying information must be removed, may compromise individual privacy. Many privacy-preserving approaches have been proposed for publishing static transactional data. Due to the characteristics of data streams, which must be processed quickly, static data anonymization methods cannot be directly applied to data streams. In this paper, we first analyze the privacy problem in publishing transactional data streams based on a sliding window. Then, we present two dynamic algorithms with generalization and suppression to anonymize continuously a sliding window to make it satisfy $\rho $ -uncertainty by structuring an affected sensitive rules trie, because the removal and addition of transactions may make the current sliding window fail to satisfy $\rho $ -uncertainty. Experimental results show that our methods are more efficient than sliding window anonymization with batch processing by using existing static anonymization methods.