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Found 14 papers, 4 papers with code
Optimal Differential Privacy Composition for Exponential Mechanisms
We consider precise composition bounds of the overall privacy loss for exponential mechanisms, one of the fundamental classes of mechanisms in DP.
Adaptive Privacy Composition for Accuracy-first Mechanisms
In many practical applications of differential privacy, practitioners seek to provide the best privacy guarantees subject to a target level of accuracy.
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints
In this work, we generalize noise reduction to the setting of Gaussian noise, introducing the Brownian mechanism.
Fully Adaptive Composition in Differential Privacy
However, these results require that the privacy parameters of all algorithms be fixed before interacting with the data.
Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics
We also provide optimal privacy loss bounds that apply when an analyst can select pure DP and bounded range mechanisms in a batch, i. e. non-adaptively.
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis
We design a general framework for answering adaptive statistical queries that focuses on providing explicit confidence intervals along with point estimates.
Practical Differentially Private Top-$k$ Selection with Pay-what-you-get Composition
We study the problem of top-$k$ selection over a large domain universe subject to user-level differential privacy.
Cryptography and Security
Protection Against Reconstruction and Its Applications in Private Federated Learning
In large-scale statistical learning, data collection and model fitting are moving increasingly toward peripheral devices---phones, watches, fitness trackers---away from centralized data collection.
Local Private Hypothesis Testing: Chi-Square Tests
We explore the design of private hypothesis tests in the local model, where each data entry is perturbed to ensure the privacy of each participant.
A New Class of Private Chi-Square Tests
In this paper, we develop new test statistics for private hypothesis testing.
Statistics Theory Cryptography and Security Statistics Theory
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing
In this paper, we initiate a principled study of how the generalization properties of approximate differential privacy can be used to perform adaptive hypothesis testing, while giving statistically valid $p$-value corrections.
Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing
We propose new tests for goodness of fit and independence testing that like the classical versions can be used to determine whether a given model should be rejected or not, and that additionally can ensure differential privacy.
Statistics Theory Cryptography and Security Statistics Theory
Do Prices Coordinate Markets?
Second, we provide learning-theoretic results that show that such prices are robust to changing the buyers in the market, so long as all buyers are sampled from the same (unknown) distribution.
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs
This models the problem of predicting the behavior of a rational agent whose goals are known, but whose resources are unknown.
