arXiv research

GDA-HIN adapts across heterogeneous networks by aligning shared and private node types.

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…

Near-optimal private tests for simple and MLR hypotheses developed under Gaussian differential privacy.

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…

Motivation: Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymisation strategies fail to provide sufficient level of protection for genomic data, because the data are inherently identifiable. Differentially private machine learning can help b…

Algorithm learns optimal coordination for strategic agents in uncertain settings.

We consider learning problems where the training set consists of two types of examples: private and public. The goal is to design a learning algorithm that satisfies differential privacy only with respect to the private examples. This setting interpolates between private learning (where all examples are private) and cl…

New methods for private statistical inference under local differential privacy.

We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data and privately train a model for generating synthetic data that …

Private two-sample tests under LDP achieve minimax rates for multinomial and continuous data.

New algorithm learns optimal policies in strategic MDPs with private types.

The process of data mining with differential privacy produces results that are affected by two types of noise: sampling noise due to data collection and privacy noise that is designed to prevent the reconstruction of sensitive information. In this paper, we consider the problem of designing confidence intervals for the…

This paper introduces differentially private permutation tests for hypothesis testing.

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…

Algorithm ensures privacy while strictly adhering to constraints.

DP-SPRT improves privacy in sequential tests with near-optimal error rates.

This work bounds the generalization error of private algorithms for discrete data.

Investor flows in Korean equity market transmit shared information, not private signals.

Paper proposes differentially private quantile regression for high-dimensional data.

New private algorithm for sequential hypothesis testing with privacy and error rate guarantees.

The paper optimizes private data sharing by selecting statistics and using MCMC for Bayesian inference.

Study differential privacy in multi-agent RL, achieving efficient and private learning.

This paper studies trade-offs in private prediction methods.

Private method measures nonlinear correlations between data hosted across two entities.

DP synthetic data may inflate statistical test results, caution advised.

New bounds for private matrix approximation using Gaussian noise and Dyson Brownian Motion.

This article presents results from the first statistically significant study of causes of cost escalation in transport infrastructure projects. The study is based on a sample of 258 rail, bridge, tunnel and road projects worth US$90 billion. The focus is on the dependence of cost escalation on (1) length of project imp…

New algorithm for personalized healthcare with privacy guarantees.

New algorithms for privately learning decision lists and halfspaces.

Private PGB boosts synthetic data quality using GANs and privacy techniques.

Hypothesis testing is one of the most common types of data analysis and forms the backbone of scientific research in many disciplines. Analysis of variance (ANOVA) in particular is used to detect dependence between a categorical and a numerical variable. Here we show how one can carry out this hypothesis test under the…

PEARL uses AI to replicate private equity performance with liquid assets.

We present a provably optimal differentially private algorithm for the stochastic multi-arm bandit problem, as opposed to the private analogue of the UCB-algorithm [Mishra and Thakurta, 2015; Tossou and Dimitrakakis, 2016] which doesn't meet the recently discovered lower-bound of $Ω\left(\frac{K\log(T)}ε \right)$ [Shar…

Public pretraining improves private model training even in extreme distribution shift scenarios.

Private learning of Gaussian Mixture Models without boundedness assumptions.

Private estimation with public data reduces sample complexity.

Matched filters reveal optimal normalization methods for different market participants.

Develops Merton's model for private companies using DDM.

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

Algorithm selects public datasets for private machine learning.

Enhances privacy in federated learning with Laplacian smoothing.

Differentially private hyperparameter tuning improves privacy in machine learning.

Optimizes private statistics with noisy methods.

Dynamic model considers private asset markets' complexities.

In this work we present novel differentially private identity (goodness-of-fit) testers for natural and widely studied classes of multivariate product distributions: Gaussians in $\mathbb{R}^d$ with known covariance and product distributions over $\{\pm 1\}^{d}$. Our testers have improved sample complexity compared to …

Bounds on Littlestone dimension for private learning and online prediction.

Private distribution learning with public data, leveraging sample compression schemes.

In recent years, the economic policy of privatization, which is defined as the transfer of property or responsibility from public sector to private sector, is one of the global phenomenon that increases use of markets to allocate resources. One important motivation for privatization is to help develop factor and produc…