TY - JOUR
T1 - PRISM
T2 - 2021 International Conference on Management of Data, SIGMOD 2021
AU - Li, Yin
AU - Ghosh, Dhrubajyoti
AU - Gupta, Peeyush
AU - Mehrotra, Sharad
AU - Panwar, Nisha
AU - Sharma, Shantanu
N1 - Funding Information:
∗This material is based on research sponsored by DARPA under agreement number FA8750-16-2-0021. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government. This work is partially supported by NSF grants 1527536 and 1545071. Yin Li is supported by the National Natural Science Foundation of China (Grant no. 61972090, 61972089, 61601396).
Publisher Copyright:
© 2021 Owner/Author.
PY - 2021
Y1 - 2021
N2 - This paper proposes Prism, a secret sharing based approach to compute private set operations (i.e., intersection and union), as well as aggregates over outsourced databases belonging to multiple owners. Prism enables data owners to pre-load the data onto non-colluding servers and exploits the additive and multiplicative properties of secret-shares to compute the above-listed operations in (at most) two rounds of communication between the servers (storing the secret-shares) and the querier, resulting in a very efficient implementation. Also, Prism does not require communication among the servers and supports result verification techniques for each operation to detect malicious adversaries. Experimental results show that Prism scales both in terms of the number of data owners and database sizes, to which prior approaches do not scale.
AB - This paper proposes Prism, a secret sharing based approach to compute private set operations (i.e., intersection and union), as well as aggregates over outsourced databases belonging to multiple owners. Prism enables data owners to pre-load the data onto non-colluding servers and exploits the additive and multiplicative properties of secret-shares to compute the above-listed operations in (at most) two rounds of communication between the servers (storing the secret-shares) and the querier, resulting in a very efficient implementation. Also, Prism does not require communication among the servers and supports result verification techniques for each operation to detect malicious adversaries. Experimental results show that Prism scales both in terms of the number of data owners and database sizes, to which prior approaches do not scale.
KW - aggregation operation
KW - private set intersection
KW - private set union
KW - result verification
UR - http://www.scopus.com/inward/record.url?scp=85108940252&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85108940252&partnerID=8YFLogxK
U2 - 10.1145/3448016.3452839
DO - 10.1145/3448016.3452839
M3 - Conference article
AN - SCOPUS:85108940252
SN - 0730-8078
SP - 1116
EP - 1128
JO - Proceedings of the ACM SIGMOD International Conference on Management of Data
JF - Proceedings of the ACM SIGMOD International Conference on Management of Data
Y2 - 20 June 2021 through 25 June 2021
ER -