arXiv research

New algorithm maintains privacy while improving model performance in selective release.

Private release of sensitive data enables fair learning.

Study private query release with public data, reducing sample sizes.

Three new oracle-efficient algorithms for private synthetic data release.

First differentially private mechanism for anonymized histograms.

New algorithm corrects bias in LDP-released data for better analysis.

New method for estimating median and mean with high probability privacy.

Paper addresses private online convex optimization with optimal algorithms in various geometries and high-dimensional bandits.

Privacy is enhanced by synthetic data release even with unlimited data.

A new method for private query release using Johnson-Lindenstrauss projection.

Private sketches protect linear regression data privacy.

This paper provides a method for noise-calibrated inference from DP synthetic data.

Transform non-private e-values into differentially private ones.

Many commonly used learning algorithms work by iteratively updating an intermediate solution using one or a few data points in each iteration. Analysis of differential privacy for such algorithms often involves ensuring privacy of each step and then reasoning about the cumulative privacy cost of the algorithm. This is …

New DP bootstrap method for statistical inference with improved privacy and accuracy.

User releases data to service provider while balancing privacy and utility.

This work refines grid size selection for non-interactive private $K$-means clustering.

Improves Bayesian linear regression for private data analysis.

Review of privacy-preserving linear models for high-dimensional data.

We lay theoretical foundations for new database release mechanisms that allow third-parties to construct consistent estimators of population statistics, while ensuring that the privacy of each individual contributing to the database is protected. The proposed framework rests on two main ideas. First, releasing (an esti…

Private machine learning framework using randomised response.

A new mechanism for differentially private Fréchet mean on SPD matrices.

Paper introduces data-dependent SSP for private linear and logistic regression.

New algorithms improve privacy-preserving data release using external predictions.

Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way. Here, we develop an end-to-end differentially private method for solving regression problems wi…

The paper analyzes and proposes methods for privately sharing individual privacy losses using per-instance differential privacy.

New algorithm for personalized healthcare with privacy guarantees.

Two private algorithms estimate Jaccard similarity efficiently.

Study differentially private methods for learning Hawkes processes.

P3GM improves privacy-preserving data synthesis for high-dimensional data.

DPpack offers R tools for private data analysis and machine learning.

Distributed devices such as mobile phones can produce and store large amounts of data that can enhance machine learning models; however, this data may contain private information specific to the data owner that prevents the release of the data. We wish to reduce the correlation between user-specific private information…

JME continually estimates data moments privately and accurately.

We investigate the problem of learning discrete, undirected graphical models in a differentially private way. We show that the approach of releasing noisy sufficient statistics using the Laplace mechanism achieves a good trade-off between privacy, utility, and practicality. A naive learning algorithm that uses the nois…

Proposes differentially private normalizing flows for privacy-preserving density estimation.

Paper proposes no-regret algorithms for private GP bandit optimization.

Algorithm ensures privacy while strictly adhering to constraints.

Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide variety of machine learning models. The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems. As machine learning becomes commonplace, Bayesian optimization bec…

Differential privacy is a framework for privately releasing summaries of a database. Previous work has focused mainly on methods for which the output is a finite dimensional vector, or an element of some discrete set. We develop methods for releasing functions while preserving differential privacy. Specifically, we sho…

Differentially private statistical inference using $β$-divergence.

Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries. On the other hand, rigorous methods such as the exponential mechanis…

We revisit the problem of differentially private release of classification queries. In this problem, the goal is to design an algorithm that can accurately answer a sequence of classification queries based on a private training set while ensuring differential privacy. We formally study this problem in the agnostic PAC …

We present differentially private algorithms for the stochastic Multi-Armed Bandit (MAB) problem. This is a problem for applications such as adaptive clinical trials, experiment design, and user-targeted advertising where private information is connected to individual rewards. Our major contribution is to show that the…

Proposes using probabilistic models for privacy-preserving synthetic data.

Widely-used public benchmarks are of huge importance to computer vision and machine learning research, especially with the computational resources required to reproduce state of the art results quickly becoming untenable. In medical image computing, the wide variety of image modalities and problem formulations yields a…

Study on generating and identifying languages privately, showing privacy imposes costs and creates barriers.

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…

Differentially private method for estimating individualized treatment rules.