TY - GEN
T1 - Scalable and secure Private Set intersection for big data
AU - Hahn, Changhee
AU - Hur, Junbeom
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) (No. 2013R1A2A2A01005559). This work was also supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea Government (MSIP) (No. B0190-15-2028 and No. R0190-15-2011).
Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84964614621&partnerID=8YFLogxK
U2 - 10.1109/BIGCOMP.2016.7425929
DO - 10.1109/BIGCOMP.2016.7425929
M3 - Conference contribution
AN - SCOPUS:84964614621
T3 - 2016 International Conference on Big Data and Smart Computing, BigComp 2016
SP - 285
EP - 288
BT - 2016 International Conference on Big Data and Smart Computing, BigComp 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Big Data and Smart Computing, BigComp 2016
Y2 - 18 January 2016 through 20 January 2016
ER -