TY - GEN

T1 - On the complexity of two-party differential privacy

AU - Haitner, Iftach

AU - Mazor, Noam

AU - Silbak, Jad

AU - Tsfadia, Eliad

N1 - Publisher Copyright:
© 2022 ACM.

PY - 2022/9/6

Y1 - 2022/9/6

N2 - In distributed differential privacy, the parties perform analysis over their joint data while preserving the privacy for both datasets. Interestingly, for a few fundamental two-party functions such as inner product and Hamming distance, the accuracy of the distributed solution lags way behind what is achievable in the client-server setting. McGregor, Mironov, Pitassi, Reingold, Talwar, and Vadhan [FOCS '10] proved that this gap is inherent, showing upper bounds on the accuracy of (any) distributed solution for these functions. These limitations can be bypassed when settling for computational differential privacy, where the data is differentially private only in the eyes of a computationally bounded observer, using oblivious transfer. We prove that the use of public-key cryptography is necessary for bypassing the limitation of McGregor et al., showing that a non-trivial solution for the inner product, or the Hamming distance, implies the existence of a key-agreement protocol. Our bound implies a combinatorial proof for the fact that non-Boolean inner product of independent (strong) Santha-Vazirani sources is a good condenser. We obtain our main result by showing that the inner-product of a (single, strong) SV source with a uniformly random seed is a good condenser, even when the seed and source are dependent.

AB - In distributed differential privacy, the parties perform analysis over their joint data while preserving the privacy for both datasets. Interestingly, for a few fundamental two-party functions such as inner product and Hamming distance, the accuracy of the distributed solution lags way behind what is achievable in the client-server setting. McGregor, Mironov, Pitassi, Reingold, Talwar, and Vadhan [FOCS '10] proved that this gap is inherent, showing upper bounds on the accuracy of (any) distributed solution for these functions. These limitations can be bypassed when settling for computational differential privacy, where the data is differentially private only in the eyes of a computationally bounded observer, using oblivious transfer. We prove that the use of public-key cryptography is necessary for bypassing the limitation of McGregor et al., showing that a non-trivial solution for the inner product, or the Hamming distance, implies the existence of a key-agreement protocol. Our bound implies a combinatorial proof for the fact that non-Boolean inner product of independent (strong) Santha-Vazirani sources is a good condenser. We obtain our main result by showing that the inner-product of a (single, strong) SV source with a uniformly random seed is a good condenser, even when the seed and source are dependent.

KW - differential privacy

KW - inner product

KW - public-key cryptography

UR - http://www.scopus.com/inward/record.url?scp=85132792989&partnerID=8YFLogxK

U2 - 10.1145/3519935.3519982

DO - 10.1145/3519935.3519982

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AN - SCOPUS:85132792989

T3 - Proceedings of the Annual ACM Symposium on Theory of Computing

SP - 1392

EP - 1405

BT - STOC 2022 - Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing

A2 - Leonardi, Stefano

A2 - Gupta, Anupam

PB - Association for Computing Machinery

T2 - 54th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2022

Y2 - 20 June 2022 through 24 June 2022

ER -