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Found 11 papers, 6 papers with code
Reinforcement Learning with Differential Privacy
Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for episodic reinforcement learning (RL).
Generating Private Synthetic Data with Genetic Algorithms
We study the problem of efficiently generating differentially private synthetic data that approximate the statistical properties of an underlying sensitive dataset.
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics
Our attacks significantly outperform those that are based only on access to a public distribution or population from which the private dataset $D$ was sampled, demonstrating that they are exploiting information in the aggregate statistics $Q(D)$, and not simply the overall structure of the distribution.
Private Synthetic Data for Multitask Learning and Marginal Queries
A key innovation in our algorithm is the ability to directly handle numerical features, in contrast to a number of related prior approaches which require numerical features to be first converted into {high cardinality} categorical features via {a binning strategy}.
Improved Regret for Differentially Private Exploration in Linear MDP
We study privacy-preserving exploration in sequential decision-making for environments that rely on sensitive data such as medical records.
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods
We study private synthetic data generation for query release, where the goal is to construct a sanitized version of a sensitive dataset, subject to differential privacy, that approximately preserves the answers to a large collection of statistical queries.
Leveraging Public Data for Practical Private Query Release
In many statistical problems, incorporating priors can significantly improve performance.
Cache Replacement as a MAB with Delayed Feedback and Decaying Costs
We derive an optimal learning rate for EXP4-DFDC that defines the balance between exploration and exploitation and proves theoretically that the expected regret of our algorithm is a vanishing quantity as a function of time.
Private Reinforcement Learning with PAC and Regret Guarantees
Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for episodic reinforcement learning (RL).
New Oracle-Efficient Algorithms for Private Synthetic Data Release
We present three new algorithms for constructing differentially private synthetic data---a sanitized version of a sensitive dataset that approximately preserves the answers to a large collection of statistical queries.
Oracle Efficient Private Non-Convex Optimization
We find that for the problem of learning linear classifiers, directly optimizing for 0/1 loss using our approach can out-perform the more standard approach of privately optimizing a convex-surrogate loss function on the Adult dataset.
