Enforcing Vocabulary k-Anonymity by Semantic Similarity Based Clustering

Abstract

Web query logs provide a rich wealth of information, but also present serious privacy risks. We consider publishing vocabularies, bags of query-terms extracted from web query logs, which has a variety of applications. We aim at preventing identity disclosure of such bag-valued data. The key feature of such data is the extreme sparsity, which renders conventional anonymization techniques not working well in retaining enough utility. We propose a semantic similarity based clustering approach to address the issue. We measure the semantic similarity between two vocabularies by a weighted bipartite matching and present a greedy algorithm to cluster vocabularies by the semantic similarities. Extensive experiments on the AOL query log show that our approach retains more data utility than existing approaches.