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Found 20 papers, 1 papers with code
Differentially Private Fair Binary Classifications
In this work, we investigate binary classification under the constraints of both differential privacy and fairness.
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity
Namely, it breaks the known lower bound of $\Omega\left(\frac{k\log k}{\alpha^2\min \{ \varepsilon^2 , 1\}} \right)$ for the sample complexity of non-interactive hypothesis selection.
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses
The Noisy-SGD algorithm is widely used for privately training machine learning models.
Contraction of Locally Differentially Private Mechanisms
We investigate the contraction properties of locally differentially private mechanisms.
The Saddle-Point Accountant for Differential Privacy
SPA approximates privacy guarantees for the composition of DP mechanisms in an accurate and fast manner.
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime
Since the optimization problem is infinite dimensional, it cannot be solved directly; nevertheless, we quantize the problem to derive near-optimal additive mechanisms that we call "cactus mechanisms" due to their shape.
Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks.
Local Differential Privacy Is Equivalent to Contraction of $E_γ$-Divergence
We investigate the local differential privacy (LDP) guarantees of a randomized privacy mechanism via its contraction properties.
Contraction of $E_γ$-Divergence and Its Applications to Privacy
First, it implies that local differential privacy can be equivalently expressed in terms of the contraction of $E_\gamma$-divergence.
Bottleneck Problems: Information and Estimation-Theoretic View
Information bottleneck (IB) and privacy funnel (PF) are two closely related optimization problems which have found applications in machine learning, design of privacy algorithms, capacity problems (e. g., Mrs. Gerber's Lemma), strong data processing inequalities, among others.
Three Variants of Differential Privacy: Lossless Conversion and Applications
In the first part, we develop a machinery for optimally relating approximate DP to RDP based on the joint range of two $f$-divergences that underlie the approximate DP and RDP.
Privacy Amplification of Iterative Algorithms via Contraction Coefficients
We investigate the framework of privacy amplification by iteration, recently proposed by Feldman et al., from an information-theoretic lens.
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$-Divergences
We derive the optimal differential privacy (DP) parameters of a mechanism that satisfies a given level of R\'enyi differential privacy (RDP).
Obfuscation via Information Density Estimation
The core of this mechanism relies on a data-driven estimate of the trimmed information density for which we propose a novel estimator, named the trimmed information density estimator (TIDE).
Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs
In this paper, we explore GNNs for graph-based SSL of histograms.
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds
Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propagating "soft labels" (e. g. probability distributions, class membership scores) over hypergraphs, by means of optimal transportation.
A Tamper-Free Semi-Universal Communication System for Deletion Channels
We investigate the problem of reliable communication between two legitimate parties over deletion channels under an active eavesdropping (aka jamming) adversarial model.
Curvature of Hypergraphs via Multi-Marginal Optimal Transport
We introduce a novel definition of curvature for hypergraphs, a natural generalization of graphs, by introducing a multi-marginal optimal transport problem for a naturally defined random walk on the hypergraph.
Generalizing Bottleneck Problems
Given a pair of random variables $(X, Y)\sim P_{XY}$ and two convex functions $f_1$ and $f_2$, we introduce two bottleneck functionals as the lower and upper boundaries of the two-dimensional convex set that consists of the pairs $\left(I_{f_1}(W; X), I_{f_2}(W; Y)\right)$, where $I_f$ denotes $f$-information and $W$ varies over the set of all discrete random variables satisfying the Markov condition $W \to X \to Y$.
Information Extraction Under Privacy Constraints
To this end, the so-called {\em rate-privacy function} is introduced to quantify the maximal amount of information (measured in terms of mutual information) that can be extracted from $Y$ under a privacy constraint between $X$ and the extracted information, where privacy is measured using either mutual information or maximal correlation.
