On the Complexity of Two-Party Differential Privacy
Iftach Haitner, Noam Mazor, Jad Silbak, Eliad Tsfadia

TL;DR
This paper investigates the inherent complexity of achieving accurate two-party differential privacy for fundamental functions like inner product and Hamming distance, establishing the necessity of cryptographic primitives for surpassing known limitations.
Contribution
It proves that bypassing known accuracy limitations in distributed differential privacy requires public-key cryptography, linking it to key-agreement protocols and condenser properties of sources.
Findings
Public-key cryptography is necessary to overcome accuracy limitations.
Inner product of certain sources acts as a good condenser.
Main result shows inner product with a random seed is a good condenser even with dependence.
Abstract
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 public-key cryptography primitives. We prove that the use of public-key cryptography is necessary for bypassing the limitation of McGregor et al., showing that a non-trivial…
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Videos
On the Complexity of Two-Party Differential Privacy· youtube
Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Privacy, Security, and Data Protection
