title: Privacy-Preserving Integration of Medical Data : A Practical Multiparty Private Set Intersection.
abstract: Medical data are often maintained by different organizations. However, detailed analyses sometimes require these datasets to be integrated without violating patient or commercial privacy. Multiparty Private Set Intersection (MPSI), which is an important privacy-preserving protocol, computes an intersection of multiple private datasets. This approach ensures that only designated parties can identify the intersection. In this paper, we propose a practical MPSI that satisfies the following requirements: The size of the datasets maintained by the different parties is independent of the others, and the computational complexity of the dataset held by each party is independent of the number of parties. Our MPSI is based on the use of an outsourcing provider, who has no knowledge of the data inputs or outputs. This reduces the computational complexity. The performance of the proposed MPSI is evaluated by implementing a prototype on a virtual private network to enable parallel computation in multiple threads. Our protocol is confirmed to be more efficient than comparable existing approaches.
authors.affiliation: Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka, Japan. miyaji@comm.eng.osaka-u.ac.jp.
authors.forename: Atsuko
authors.initials: A
authors.lastname: Miyaji
authors.affiliation: Department of Machine Intelligence and Systems Engineering, Akita Prefectural University, 84-4 Ebinokuchi, Tsuchiya, Yurihonjo, Akita, Japan.
authors.forename: Kazuhisa
authors.initials: K
authors.lastname: Nakasho
authors.affiliation: Japan Advanced Institute of Science and Technology, Asahidai 1-1, Nomi-shi, Ishikawa, Japan.
authors.forename: Shohei
authors.initials: S
authors.lastname: Nishida
mesh_terms: D000465:Algorithms; D016494:Computer Security / Q000295:instrumentation*; D003219:Confidentiality; D063025:Health Information Management / Q000295:instrumentation*; D006801:Humans
keywords: Medical data; Privacy-preserving data integration; Private set intersection