Optimizes differentially private kernel learning with random projection.
problem Privacy-preserving learning algorithms with optimal performance.
method Differentially private kernel ERM algorithm based on random projection in reproducing kernel Hilbert space.
result Achieves minimax-optimal excess risk rates for various loss functions.
New framework reduces private mean estimation error with optimal efficiency.
problem Locally private mean estimation of high-dimensional vectors.
method ProjUnit framework: random projections, normalization, and optimal algorithm execution in lower dimensions.
result Optimal error up to a 1+o(1)-factor with computational efficiency and low communication complexity.
New methods for private statistical inference under local differential privacy.
problem Private statistical inference for population means with bounded observations.
method Nonparametric, nonasymptotic statistical inference using a generalized randomized response mechanism.
result Private confidence intervals and sequences for population means under LDP constraints.
DiPriMe forests use private medians to create balanced tree splits for privacy-protected data.
problem Privacy concerns in training random forests due to multiple data queries.
method Proposes DiPriMe forests, which use a private median to generate balanced splits, ensuring differential privacy.
result DiPriMe forests achieve high utility while maintaining differential privacy, as shown both theoretically and empirically.
New insights into image compression trade-offs with private randomness.
problem Trade-off between compression rate and perceptual quality in image compression.
method Characterization of rate-distortion trade-off with private randomness under different realism constraints.
result Encoder private randomness is not useful if compression rate is below source entropy, even with limited common and decoder private randomness.
DP-RandP improves privacy-utility tradeoff in DP-SGD by learning priors from random processes.
problem Improving the performance of differentially private stochastic gradient descent (DP-SGD) on private data.
method A three-phase approach that learns priors from images generated by random processes and transfers these priors to private data.
result New state-of-the-art accuracy on CIFAR10, CIFAR100, MedMNIST, and ImageNet for various privacy budgets.
Differentially private random block coordinate descent improves utility in machine learning.
problem Lack of privacy in classical CD methods when handling sensitive information.
method Proposes a differentially private random block coordinate descent method using sketch matrices and importance sampling.
result Demonstrates improved convergence rates and utility guarantees compared to non-private methods.
Hermite polynomials improve private data generation by reducing feature count.
problem Infinite-dimensional features in kernel mean embedding are impractical for private data generation.
method Replace random features with Hermite polynomial features, leveraging their ordered nature.
result Hermite polynomial features yield a more accurate approximation of kernel mean embedding with fewer features.
Differentially private hyperparameter tuning improves privacy in machine learning.
problem Hyperparameter tuning leaks private information through selected configurations.
method Local Bayesian optimization using Gaussian Process surrogate for private gradient approximation.
result DP-GIBO converges to locally optimal hyperparameters with polynomial dimensional dependence.
Improved privacy and utility in machine learning with adaptive differential privacy.
problem Enhancing privacy in machine learning models while maintaining utility.
method Adaptive differentially private (ADP) learning method that optimally adapts noise to stepsize.
result ADP method significantly improves utility compared to standard differentially private methods.
Popular approaches to differential privacy, such as the Laplace and exponential mechanisms, calibrate randomised smoothing through global sensitivity of the target non-private function. Bounding such sensitivity is often a prohibitively complex analytic calculation. As an alternative, we propose a straightforward sampl…
Develops a new deep learning framework for privacy-preserving text representations.
problem Privacy concerns in deep learning frameworks requiring data pooling to a trusted server.
method Three modules: embedding, randomization, and classifier. Novel LDP protocol reduces privacy impact on accuracy.
result Framework delivers comparable or better performance than non-private and existing LDP protocols.
We propose a novel framework for the differentially private ERM, input perturbation. Existing differentially private ERM implicitly assumed that the data contributors submit their private data to a database expecting that the database invokes a differentially private mechanism for publication of the learned model. In i…
DPlis improves privacy in deep learning models by smoothing loss functions.
problem Privacy leakage in deep learning models trained on private data and low model performance.
method DPlis constructs a smooth loss function to favor noise-resilient models.
result DPlis effectively boosts model quality and training stability under privacy constraints.
Paper bridges statistical inference for DP-SGD, a privacy-preserving machine learning method.
problem Asymptotic statistical inference for Differentially Private Stochastic Gradient Descent (DP-SGD).
method Established asymptotic properties of SGD under randomized subsampling, extended to DP-SGD, proposed methods for constructing valid confidence intervals.
result Valid confidence intervals for DP-SGD output achieve nominal coverage rates while maintaining privacy.
Locally private online quantile regression method addresses privacy constraints.
problem Estimating and inferring quantile regression under local differential privacy constraints.
method Developed a finite-alphabet channel where users compute local contributions, apply randomized response, and send reports. A public decoder corrects distortion and reconstructs inputs for averaging.
result Established local privacy, decoder unbiasedness, consistency, asymptotic normality, and inference for scalar contrasts.
We design two mechanisms for the recommender system to collect user ratings. One is modified Laplace mechanism, and the other is randomized response mechanism. We prove that they are both differentially private and preserve the data utility.
Two private algorithms estimate Jaccard similarity efficiently.
problem Estimating Jaccard similarity while preserving user privacy.
method Extends MinHash with Generalized Randomized Response and Laplace Mechanism.
result Achieves privacy-utility trade-off with theoretical bounds and experiments.
We investigate the problems of identity and closeness testing over a discrete population from random samples. Our goal is to develop efficient testers while guaranteeing Differential Privacy to the individuals of the population. We describe an approach that yields sample-efficient differentially private testers for the…
New algorithm for differential private kernel bandits with improved regret bounds.
problem Contextual bandits with differential privacy constraints.
method Proposes a private kernel-ridge regression estimator using random projections.
result Achieves state-of-the-art cumulative regret bounds in both joint and local models of differential privacy.
New sampling scheme improves privacy in DP-SGD without sacrificing utility.
problem Suboptimal privacy amplification due to participation variance in Poisson subsampling.
method Balanced Iteration Subsampling (BIS) with structured randomness.
result BIS achieves stronger privacy amplification than Poisson subsampling and is optimal at both extremes of noise spectrum.
Efficient privacy-preserving machine learning framework using random transformations.
problem Slow training and inference speed in privacy-preserving machine learning systems.
method Random transformations like linear and permutation, combined with arithmetic sharing.
result High efficiency and low computation cost in private machine learning.
Paper proposes a differentially private test for joint dependence among random vectors.
problem Detecting joint dependence among sensitive data while maintaining privacy.
method Differentially private permutation methodology for dHSIC test.
result Proposed test attains minimax optimal power across privacy regimes.
We derive concentration inequalities for differentially private median and mean estimators building on the "Propose, Test, Release" (PTR) mechanism introduced by Dwork and Lei (2009). We introduce a new general version of the PTR mechanism that allows us to derive high probability error bounds for differentially privat…
Polynomial-time algorithm estimates edge density of random graphs with privacy and robustness.
problem Estimating edge density of random graphs while maintaining privacy and robustness.
method Sum-of-squares algorithm for robust edge density estimation and reduction from privacy to robustness.
result Optimal error rate up to logarithmic factors, matching theoretical lower bounds.
First private Bayesian optimization algorithm with provable performance.
problem Privacy-preserving Bayesian optimization in outsourced settings.
method Random projection-based transformation to preserve privacy and distances.
result Regret bound similar to non-private GP-UCB established for PO-GP-UCB.
This work proposes a novel privacy-preserving method for synthetic replacement of sensitive data.
problem Privacy-preserving transformations of sensitive data.
method Adversarial representation learning for synthetic replacement of private attributes.
result Our method provides stronger privacy and better utility than previous methods.
Develops a private synthetic graph generator using Gromov-Wasserstein distance.
problem Creating private synthetic networks for complex data.
method Random connection model, fused Gromov-Wasserstein distance, differential privacy.
result Effective algorithm for generating private synthetic graphs with theoretical guarantees.
In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running …
We consider a network of agents that aim to learn some unknown state of the world using private observations and exchange of beliefs. At each time, agents observe private signals generated based on the true unknown state. Each agent might not be able to distinguish the true state based only on her private observations.…
Proposes DP-MERF for privacy-preserving synthetic data generation.
problem Privacy-preserving data generation for synthetic datasets.
method Differentially private mean embeddings with random features.
result Achieves better privacy-utility trade-offs than existing methods.
Study differentially private methods for learning Hawkes processes.
problem Lack of thorough analysis on sample complexity for learning Hawkes processes parameters and releasing differentially private versions.
method Developed non-private and differentially private estimators for Hawkes processes parameters.
result Obtained sample complexity results for both private and non-private settings.
We introduce a simple framework for designing private boosting algorithms. We give natural conditions under which these algorithms are differentially private, efficient, and noise-tolerant PAC learners. To demonstrate our framework, we use it to construct noise-tolerant and private PAC learners for large-margin halfspa…
The paper proposes a method to learn differentially private variational autoencoders with term-wise gradient aggregation.
problem Learning variational autoencoders with differential privacy constraints and multiple divergences.
method Term-wise Differentially Private SGD (DP-SGD) that crafts randomized gradients for each loss term, keeping sensitivity at O(1).
result The method reduces the amount of noise needed for differential privacy, allowing better learning.
Cluster-DP improves differential privacy in randomized experiments by clustering data.
problem Reducing variance in causal effect estimation from differentially private data.
method Cluster-DP leverages a given cluster structure to improve the privacy-variance trade-off.
result Selecting higher-quality clusters decreases the variance penalty without compromising privacy guarantees.
New method improves deep CCA by modeling private components conditionally independent of common factors.
problem Discovering latent co-variation in multiview datasets with weak common factors.
method Proposes a novel formulation that models private components conditionally independent of common factors.
result Validates the approach with synthetic and real datasets, showing improved identification of common factors.
Differential privacy is a cryptographically-motivated definition of privacy which has gained significant attention over the past few years. Differentially private solutions enforce privacy by adding random noise to a function computed over the data, and the challenge in designing such algorithms is to control the added…
Unified approach improves accuracy in private estimation.
problem Protecting sensitive information in statistical estimation.
method Unified Bayes Private Point (UBaPP) approach to Bayes point estimation under differential privacy constraints.
result Achieves better accuracy-privacy trade-off than traditional methods.
Zeroth-order optimization methods lack inherent privacy guarantees.
problem Ensuring differential privacy in zeroth-order optimization methods.
method Analyzing ZO-GD with and without random initialization for convex and strongly convex objectives.
result ZO-GD is not differentially private for strongly convex objectives and can have superlinear privacy loss.
Develops a simple method for creating private confidence intervals.
problem Creating private confidence intervals for parametric estimation.
method Parametric bootstrap approach to construct confidence intervals.
result The parametric bootstrap provides consistent and effective confidence intervals.
We describe a framework for designing efficient active learning algorithms that are tolerant to random classification noise and are differentially-private. The framework is based on active learning algorithms that are statistical in the sense that they rely on estimates of expectations of functions of filtered random e…
Paper proposes no-regret algorithms for private GP bandit optimization.
problem Private Gaussian process bandit optimization.
method Combines uniform kernel approximator with random perturbations for differentially private GP bandit algorithms.
result Provable no-regret algorithms for stationary kernel functions in two DP settings.
We tackle the problem of estimating a location parameter with differential privacy guarantees and sub-Gaussian deviations. Recent work in statistics has focused on the study of estimators that achieve sub-Gaussian type deviations even for heavy tailed data. We revisit some of these estimators through the lens of differ…
Privacy-preserving machine learning methods add randomness, leading to varying predictions.
problem Privacy-preserving machine learning methods add randomness, leading to varying predictions.
method The study analyzes three DP-ensuring algorithms: output perturbation, objective perturbation, and DP-SGD.
result The degree of predictive multiplicity rises as the level of privacy increases, and is unevenly distributed across individuals and demographic groups.
Private CI tests for continuous Z with privacy constraints.
problem Testing conditional independence under differential privacy constraints.
method Developed two private CI testing procedures based on generalized covariance and conditional randomization tests.
result First private CI tests with rigorous theoretical guarantees for continuous Z.
We consider the problem of learning Markov Random Fields (including the prototypical example, the Ising model) under the constraint of differential privacy. Our learning goals include both structure learning, where we try to estimate the underlying graph structure of the model, as well as the harder goal of parameter l…
DP-SGD can update fewer coordinates while maintaining privacy.
problem How to update fewer coordinates in DP-SGD without losing optimization signal.
method TP-TopK (Two-Phase TopK DP-SGD), a two-phase method for coordinate-sparse private training.
result Private training can update fewer coordinates without losing optimization signal, scaling noise with active dimension \(k\) instead of full dimension \(d\).
Locally private methods detect changes in time series data.
problem Detecting distributional changes in time series data under local differential privacy.
method Proposed locally differentially private algorithms based on randomized response and binary mechanisms.
result Theoretical performance bounds and empirical validation of detection accuracy.