TY - JOUR
T1 - EMPSI
T2 - Efficient multiparty private set intersection (with cardinality)
AU - Yang, Yunbo
AU - Dong, Xiaolei
AU - Cao, Zhenfu
AU - Shen, Jiachen
AU - Li, Ruofan
AU - Yang, Yihao
AU - Dou, Shangmin
N1 - Publisher Copyright:
© 2024, Higher Education Press.
PY - 2024/2
Y1 - 2024/2
N2 - Multiparty private set intersection (PSI) allows several parties, each holding a set of elements, to jointly compute the intersection without leaking any additional information. With the development of cloud computing, PSI has a wide range of applications in privacy protection. However, it is complex to build an efficient and reliable scheme to protect user privacy. To address this issue, we propose EMPSI, an efficient PSI (with cardinality) protocol in a multiparty setting. EMPSI avoids using heavy cryptographic primitives (mainly rely on symmetric-key encryption) to achieve better performance. In addition, both PSI and PSI with the cardinality of EMPSI are secure against semi-honest adversaries and allow any number of colluding clients (at least one honest client). We also do experiments to compare EMPSI with some state-of-the-art works. The experimental results show that proposed EMPSI (-CA) has better performance and is scalable in the number of clients and the set size.
AB - Multiparty private set intersection (PSI) allows several parties, each holding a set of elements, to jointly compute the intersection without leaking any additional information. With the development of cloud computing, PSI has a wide range of applications in privacy protection. However, it is complex to build an efficient and reliable scheme to protect user privacy. To address this issue, we propose EMPSI, an efficient PSI (with cardinality) protocol in a multiparty setting. EMPSI avoids using heavy cryptographic primitives (mainly rely on symmetric-key encryption) to achieve better performance. In addition, both PSI and PSI with the cardinality of EMPSI are secure against semi-honest adversaries and allow any number of colluding clients (at least one honest client). We also do experiments to compare EMPSI with some state-of-the-art works. The experimental results show that proposed EMPSI (-CA) has better performance and is scalable in the number of clients and the set size.
UR - https://www.scopus.com/pages/publications/85169978156
U2 - 10.1007/s11704-022-2269-0
DO - 10.1007/s11704-022-2269-0
M3 - 文章
AN - SCOPUS:85169978156
SN - 2095-2228
VL - 18
JO - Frontiers of Computer Science
JF - Frontiers of Computer Science
IS - 1
M1 - 181804
ER -