In this paper we study the privacy preservation properties of a specific technique for query log anonymization: token-based hashing. In this approach, each query is tokenized, and then a secure hash function is applied to each token. We show that statistical linguistic techniques may be applied to partially compromise the anonymization. We then analyze the specific risks that arise from these partial compromises, focused on revelation of identity from unambiguous names, addresses, and so forth, and the revelation of facts associated with an identity that are deemed to be highly sensitive. Our goal in this work is twofold: to show that token-based hashing is unsuitable for anonymization, and to present a concrete risk analysis framework for evaluating other proposals.
Slot:
Shaughnessy, Friday, May 11, 2007, 5:00pm to 5:30pm.