arXiv research

BCDP enhances privacy by protecting sensitive features more precisely.

We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We adapt the Bayesian privacy accounting method to the federated sett…

A novel method for learning Bayesian network structures from decentralized data, balancing privacy and efficiency.

Noise-aware Bayesian inference framework for locally private data collection.

New MCMC method estimates differential privacy from multiple MIAs without worst-case assumptions.

A new method for privacy-preserving Bayesian learning in federated learning.

Paper presents a fast, private MH algorithm for large-scale Bayesian inference.

Traditional differential privacy is independent of the data distribution. However, this is not well-matched with the modern machine learning context, where models are trained on specific data. As a result, achieving meaningful privacy guarantees in ML often excessively reduces accuracy. We propose Bayesian differential…

We consider the problem of Bayesian learning on sensitive datasets and present two simple but somewhat surprising results that connect Bayesian learning to "differential privacy:, a cryptographic approach to protect individual-level privacy while permiting database-level utility. Specifically, we show that that under s…

DP-BNNs improve accuracy, privacy, and reliability in neural networks.

FSL-BDP models time-to-default without centralizing data, improving privacy mechanisms in federated settings.

Significant success has been realized recently on applying machine learning to real-world applications. There have also been corresponding concerns on the privacy of training data, which relates to data security and confidentiality issues. Differential privacy provides a principled and rigorous privacy guarantee on mac…

We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on proba-bilistic graphical models. These include two mechanisms for adding noise to the Bayesia…

d3p package enables efficient Bayesian inference with differential privacy.

Differentially private hyperparameter tuning improves privacy in machine learning.

Enhances privacy in data annotation and inspection with synthetic data.

Gaussian processes with differential privacy protect both inputs and outputs.

Bayesian VFLMSP improves multimodal survival prediction with privacy.

We develop a novel approximate Bayesian computation (ABC) framework, ABCDP, that produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the Sparse Vector Technique (SVT), widely studied in the differential privacy literature. SVT incurs the privacy cost only when a cond…

Bayesian inference has great promise for the privacy-preserving analysis of sensitive data, as posterior sampling automatically preserves differential privacy, an algorithmic notion of data privacy, under certain conditions (Dimitrakakis et al., 2014; Wang et al., 2015). While this one posterior sample (OPS) approach e…

The paper proposes methods to infer from privacy-protected data using simulation-based techniques.

Large data collections required for the training of neural networks often contain sensitive information such as the medical histories of patients, and the privacy of the training data must be preserved. In this paper, we introduce a dropout technique that provides an elegant Bayesian interpretation to dropout, and show…

Adaptive truncation improves privacy in online Bayesian estimation.

Differential privacy formalises privacy-preserving mechanisms that provide access to a database. We pose the question of whether Bayesian inference itself can be used directly to provide private access to data, with no modification. The answer is affirmative: under certain conditions on the prior, sampling from the pos…

New method for GLMs under DP provides private uncertainty quantification.

Noise-aware DP inference improves accuracy for complex models.

LDP is equivalent to contraction of E_γ-divergence, impacting privacy and utility.

AdOBEst-LDP improves privacy-preserving frequency estimation for categorical data.

A novel Bayesian framework for private linear regression with MCMC.

A new framework for private Bayesian tests maintains interpretability and computational efficiency.

Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off privacy and utility in a principled way. Quantifying this trade-off in advance is essential to decision-makers tasked with deciding how much p…

Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. In this paper, we further extend the applicability of DP Bayesian learning by presenting the first general DP Markov chain Monte Carlo (MCMC)…

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

Linear regression is an important tool across many fields that work with sensitive human-sourced data. Significant prior work has focused on producing differentially private point estimates, which provide a privacy guarantee to individuals while still allowing modelers to draw insights from data by estimating regressio…

We study $E_γ$-divergence contraction and its privacy implications.

In many real-world applications of machine learning, data are distributed across many clients and cannot leave the devices they are stored on. Furthermore, each client's data, computational resources and communication constraints may be very different. This setting is known as federated learning, in which privacy is a …

FedLog reduces communication in federated learning by sharing data summaries.

Deep neural networks with their large number of parameters are highly flexible learning systems. The high flexibility in such networks brings with some serious problems such as overfitting, and regularization is used to address this problem. A currently popular and effective regularization technique for controlling the…

Many machine learning applications are based on data collected from people, such as their tastes and behaviour as well as biological traits and genetic data. Regardless of how important the application might be, one has to make sure individuals' identities or the privacy of the data are not compromised in the analysis.…

Many applications of Bayesian data analysis involve sensitive information, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method. Our framework respects differential privacy, t…

Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entir…

New method compares DP mechanisms beyond $(\varepsilon, δ)$ pairs.

Differentially private statistical inference using $β$-divergence.

This work addresses privacy in Bayesian estimation, achieving near-optimal error rates.

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…

New DP mechanism SWAG-PPM improves privacy in deep learning models.

In statistical learning, a dataset is often partitioned into two parts: the training set and the holdout (i.e., testing) set. For instance, the training set is used to learn a predictor, and then the holdout set is used for estimating the accuracy of the predictor on the true distribution. However, often in practice, t…

Privacy subsidy found in market trading with noisy direction signals.