no code implementations • 22 Mar 2023 • Mark Bun, Marco Gaboardi, Max Hopkins, Russell Impagliazzo, Rex Lei, Toniann Pitassi, Satchit Sivakumar, Jessica Sorrell
In particular, we give sample-efficient algorithmic reductions between perfect generalization, approximate differential privacy, and replicability for a broad class of statistical problems.
no code implementations • 17 Aug 2021 • Cheng Zhang, Arthur Azevedo de Amorim, Marco Gaboardi
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.
no code implementations • NeurIPS 2021 • Mark Bun, Marco Gaboardi, Satchit Sivakumar
We show a generic reduction from multiclass differentially private PAC learning to binary private PAC learning.
no code implementations • NeurIPS 2021 • Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, Lydia Zakynthinou
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.
no code implementations • 11 Nov 2020 • Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu
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 $p$.
no code implementations • 24 Jul 2020 • Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan, Jayshree Sarathy
Sampling schemes are fundamental tools in statistics, survey design, and algorithm design.
no code implementations • NeurIPS 2019 • Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang
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.
no code implementations • 1 Oct 2019 • Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu
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.
no code implementations • NeurIPS 2019 • Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek
A fundamental result in differential privacy states that the privacy guarantees of a mechanism are preserved by any post-processing of its output.
no code implementations • 24 May 2019 • Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, Tetsuya Sato
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.
no code implementations • NeurIPS 2018 • Di Wang, Marco Gaboardi, Jinhui Xu
In this paper, we revisit the Empirical Risk Minimization problem in the non-interactive local model of differential privacy.
no code implementations • NeurIPS 2018 • Borja Balle, Gilles Barthe, Marco Gaboardi
Differential privacy comes equipped with multiple analytical tools for the design of private data analyses.
no code implementations • ICML 2018 • Marco Gaboardi, Ryan Rogers
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.
no code implementations • NeurIPS 2018 • Di Wang, Marco Gaboardi, Jinhui Xu
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].
no code implementations • 15 Mar 2017 • Alejandro Aguirre, Gilles Barthe, Marco Gaboardi, Deepak Garg, Pierre-Yves Strub
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
3 code implementations • 14 Sep 2016 • Marco Gaboardi, James Honaker, Gary King, Jack Murtagh, Kobbi Nissim, Jonathan Ullman, Salil Vadhan
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
1 code implementation • 7 Feb 2016 • Marco Gaboardi, Hyun woo Lim, Ryan Rogers, Salil Vadhan
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
2 code implementations • 16 Mar 2015 • Arthur Azevedo de Amorim, Emilio Jesús Gallego Arias, Marco Gaboardi, Justin Hsu
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
1 code implementation • 13 Feb 2015 • Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub
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
1 code implementation • 25 Jul 2014 • Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub
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
no code implementations • 6 Feb 2014 • Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu
We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets.