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Found 6 papers, 0 papers with code
Confidential Federated Computations
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data.
Shuffle Private Stochastic Convex Optimization
In shuffle privacy, each user sends a collection of randomized messages to a trusted shuffler, the shuffler randomly permutes these messages, and the resulting shuffled collection of messages must satisfy differential privacy.
The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation
There has been a recent wave of interest in intermediate trust models for differential privacy that eliminate the need for a fully trusted central data collector, but overcome the limitations of local differential privacy.
Private Query Release Assisted by Public Data
In comparison, with only private samples, this problem cannot be solved even for simple query classes with VC-dimension one, and without any private samples, a larger public sample of size $d/\alpha^2$ is needed.
Connecting Robust Shuffle Privacy and Pan-Privacy
First, we give robustly shuffle private protocols and upper bounds for counting distinct elements and uniformity testing.
Skyline Identification in Multi-Armed Bandits
There is an ordered set of $n$ arms $A[1],\dots, A[n]$, each with some stochastic reward drawn from some unknown bounded distribution.
