Private record matching using differential privacy

Private matching between datasets owned by distinct parties is a challenging problem with several applications. Private matching allows two parties to identify the records that are close to each other according to some distance functions, such that no additional information other than the join result is disclosed to any party. Private matching can be solved securely and accurately using secure multi-party computation (SMC) techniques, but such an approach is prohibitively expensive in practice. Previous work proposed the release of sanitized versions of the sensitive datasets which allows blocking, i.e., filtering out sub-sets of records that cannot be part of the join result. This way, SMC is applied only to a small fraction of record pairs, reducing the matching cost to acceptable levels. The blocking step is essential for the privacy, accuracy and efficiency of matching. However, the state-of-the-art focuses on sanitization based on k-anonymity, which does not provide sufficient...

• Type: Journal
• Year: 2010
• Where: EDBT
• Authors: Ali Inan, Murat Kantarcioglu, Gabriel Ghinita, Elisa Bertino
• Keywords: Database, Database Management, EDBT 2010, Privacy, Private Matching