2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
Changhee Hahn , Department of Computer Science and Engineering, Korea University, Seoul, 136-701, Rep. of Korea
Junbeom Hur , Department of Computer Science and Engineering, Korea University, Seoul, 136-701, Rep. of Korea
In this paper, we investigate Private Set Intersection (PSI) schemes that can be used to output intersection data between a client and a server in a way that only the client learns the output at the end of their joint computation. Recently, Dong et al. proposed a Bloom filter-based PSI scheme for big data. We show that a malicious client is able to learn not only the intersection but other part of the server's set in Dong et al.'s scheme. This can be delivered by submitting arbitrary Bloom filters as inputs. To this end, we suggest a Merkle tree-based countermeasure. It prevents malicious clients from learning any part of the servers set except the intersection. The security and performance analysis shows that our scheme is secure against the malicious client with a minor efficiency degradation.
Servers, Cryptography, Protocols, Indexes, Big data, Privacy
Changhee Hahn and J. Hur, "Scalable and secure Private Set intersection for big data," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 285-288.