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Found 13 papers, 1 papers with code
Easy Differentially Private Linear Regression
In this paper, we study an algorithm which uses the exponential mechanism to select a model with high Tukey depth from a collection of non-private regression models.
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.
Connecting Robust Shuffle Privacy and Pan-Privacy
First, we give robustly shuffle private protocols and upper bounds for counting distinct elements and uniformity testing.
Pan-Private Uniformity Testing
We show that the sample complexity of pure pan-private uniformity testing is $\Theta(k^{2/3})$.
Exponential Separations in Local Differential Privacy
We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues.
The Role of Interactivity in Local Differential Privacy
Next, we show that our reduction is tight by exhibiting a family of problems such that for any $k$, there is a fully interactive $k$-compositional protocol which solves the problem, while no sequentially interactive protocol can solve the problem without at least an $\tilde \Omega(k)$ factor more examples.
Locally Private Gaussian Estimation
We study a basic private estimation problem: each of $n$ users draws a single i. i. d.
Local Differential Privacy for Evolving Data
Moreover, existing techniques to mitigate this effect do not apply in the "local model" of differential privacy that these systems use.
A Convex Framework for Fair Regression
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems.
Fairness in Reinforcement Learning
We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards.
Fairness in Learning: Classic and Contextual Bandits
This tight connection allows us to provide a provably fair algorithm for the linear contextual bandit problem with a polynomial dependence on the dimension, and to show (for a different class of functions) a worst-case exponential gap in regret between fair and non-fair learning algorithms
