Search Results for author:
Found 21 papers, 5 papers with code
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization
In particular, we give sample-efficient algorithmic reductions between perfect generalization, approximate differential privacy, and replicability for a broad class of statistical problems.
On Incorrectness Logic and Kleene Algebra with Top and Tests
In his seminal work, Kozen proved that KAT subsumes propositional Hoare logic, showing that one can reason about the (partial) correctness of while programs by means of the equational theory of KAT.
Multiclass versus Binary Differentially Private PAC Learning
We show a generic reduction from multiclass differentially private PAC learning to binary private PAC learning.
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
Each of our estimators is based on a simple, general approach to designing differentially private mechanisms, but with novel technical steps to make the estimator private and sample-efficient.
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
In our second attempt, we show that for any $1$-Lipschitz generalized linear convex loss function, there is an $(\epsilon, \delta)$-LDP algorithm whose sample complexity for achieving error $\alpha$ is only linear in the dimensionality $p$.
Controlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling
Sampling schemes are fundamental tools in statistics, survey design, and algorithm design.
Facility Location Problem in Differential Privacy Model Revisited
On the negative side, we show that the approximation ratio of any $\epsilon$-DP algorithm is lower bounded by $\Omega(\frac{1}{\sqrt{\epsilon}})$, even for instances on HST metrics with uniform facility cost, under the super-set output setting.
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
In the second part of the paper, we extend our idea to the problem of estimating non-linear regressions and show similar results as in GLMs for both multivariate Gaussian and sub-Gaussian cases.
Privacy Amplification by Mixing and Diffusion Mechanisms
A fundamental result in differential privacy states that the privacy guarantees of a mechanism are preserved by any post-processing of its output.
Hypothesis Testing Interpretations and Renyi Differential Privacy
These conditions are useful to analyze the distinguishability power of divergences and we use them to study the hypothesis testing interpretation of some relaxations of differential privacy based on Renyi divergence.
Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited
In this paper, we revisit the Empirical Risk Minimization problem in the non-interactive local model of differential privacy.
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Differential privacy comes equipped with multiple analytical tools for the design of private data analyses.
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.
Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case
In the case of constant or low dimensionality ($p\ll n$), we first show that if the ERM loss function is $(\infty, T)$-smooth, then we can avoid a dependence of the sample complexity, to achieve error $\alpha$, on the exponential of the dimensionality $p$ with base $1/\alpha$ (i. e., $\alpha^{-p}$), which answers a question in [smith 2017 interaction].
A Relational Logic for Higher-Order Programs
Relational program verification can be used for reasoning about a broad range of properties, including equivalence and refinement, and specialized notions such as continuity, information flow security or relative cost.
Programming Languages
PSI (Ψ): a Private data Sharing Interface
We provide an overview of PSI ("a Private data Sharing Interface"), a system we are developing to enable researchers in the social sciences and other fields to share and explore privacy-sensitive datasets with the strong privacy protections of differential privacy.
Cryptography and Security Computers and Society Methodology
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
Really Natural Linear Indexed Type Checking
A natural way to enhance the expressiveness of this approach is by allowing the indices to depend on runtime information, in the spirit of dependent types.
Logic in Computer Science
Computer-aided verification in mechanism design
To address both concerns, we explore techniques from computer-aided verification to construct formal proofs of incentive properties.
Computer Science and Game Theory Logic in Computer Science
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy
Unlike typical programmatic properties, it is not sufficient for algorithms to merely satisfy the property---incentive properties are only useful if the strategic agents also believe this fact.
Programming Languages Computer Science and Game Theory
Dual Query: Practical Private Query Release for High Dimensional Data
We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets.
