On the Complexity of Two-Party Differential Privacy. (arXiv:2108.07664v3 [cs.CR] UPDATED)

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 …

complexity differential privacy party privacy