Search Results for author:
Found 33 papers, 13 papers with code
On the Privacy of Selection Mechanisms with Gaussian Noise
In this work, we revisit the analysis of Report Noisy Max and Above Threshold with Gaussian noise and show that, under the additional assumption that the underlying queries are bounded, it is possible to provide pure ex-ante DP bounds for Report Noisy Max and pure ex-post DP bounds for Above Threshold.
Unlocking Accuracy and Fairness in Differentially Private Image Classification
The poor performance of classifiers trained with DP has prevented the widespread adoption of privacy preserving machine learning in industry.
Differentially Private Diffusion Models Generate Useful Synthetic Images
By privately fine-tuning ImageNet pre-trained diffusion models with more than 80M parameters, we obtain SOTA results on CIFAR-10 and Camelyon17 in terms of both FID and the accuracy of downstream classifiers trained on synthetic data.
Tight Auditing of Differentially Private Machine Learning
Moreover, our auditing scheme requires only two training runs (instead of thousands) to produce tight privacy estimates, by adapting recent advances in tight composition theorems for differential privacy.
Extracting Training Data from Diffusion Models
Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted significant attention due to their ability to generate high-quality synthetic images.
Unlocking High-Accuracy Differentially Private Image Classification through Scale
Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training points.
Classification
Image Classification with Differential Privacy
+1
Reconstructing Training Data with Informed Adversaries
Our work provides an effective reconstruction attack that model developers can use to assess memorization of individual points in general settings beyond those considered in previous works (e. g. generative language models or access to training gradients); it shows that standard models have the capacity to store enough information to enable high-fidelity reconstruction of training data points; and it demonstrates that differential privacy can successfully mitigate such attacks in a parameter regime where utility degradation is minimal.
Learning to be adversarially robust and differentially private
We study the difficulties in learning that arise from robust and differentially private optimization.
Ethical and social risks of harm from Language Models
We discuss the points of origin of different risks and point to potential mitigation approaches.
A Law of Robustness for Weight-bounded Neural Networks
Our result coincides with that conjectured in (Bubeck et al., 2020) for two-layer networks under the assumption of bounded weights.
Private Reinforcement Learning with PAC and Regret Guarantees
Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for episodic reinforcement learning (RL).
Privacy Amplification via Random Check-Ins
It has privacy/accuracy trade-offs similar to privacy amplification by subsampling/shuffling.
A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES
Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results.
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations
We conduct privacy audit experiments against 2 baseline models and utility experiments on 3 datasets to demonstrate the tradeoff between privacy and utility for varying values of epsilon on different task types.
Actor Critic with Differentially Private Critic
Reinforcement learning algorithms are known to be sample inefficient, and often performance on one task can be substantially improved by leveraging information (e. g., via pre-training) on other related tasks.
Differentially Private Summation with Multi-Message Shuffling
In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for $n$-party real summation in the shuffle model of differential privacy with $O_{\epsilon, \delta}(1)$ error and $\Theta(\epsilon\sqrt{n})$ one-bit messages per party.
Privacy Amplification by Mixing and Diffusion Mechanisms
A fundamental result in differential privacy states that the privacy guarantees of a mechanism are preserved by any post-processing of its output.
Model-Agnostic Counterfactual Explanations for Consequential Decisions
Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval.
Automatic Discovery of Privacy-Utility Pareto Fronts
Differential privacy is a mathematical framework for privacy-preserving data analysis.
Hypothesis Testing Interpretations and Renyi Differential Privacy
These conditions are useful to analyze the distinguishability power of divergences and we use them to study the hypothesis testing interpretation of some relaxations of differential privacy based on Renyi divergence.
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification
Active learning holds promise of significantly reducing data annotation costs while maintaining reasonable model performance.
Continual Learning in Practice
This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives.
The Privacy Blanket of the Shuffle Model
Additionally, Erlingsson et al. (SODA 2019) provide a privacy amplification bound quantifying the level of curator differential privacy achieved by the shuffle model in terms of the local differential privacy of the randomizer used by each user.
Hierarchical Methods of Moments
Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models.
Subsampled Rényi Differential Privacy and Analytical Moments Accountant
We study the problem of subsampling in differential privacy (DP), a question that is the centerpiece behind many successful differentially private machine learning algorithms.
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Differential privacy comes equipped with multiple analytical tools for the design of private data analyses.
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
The Gaussian mechanism is an essential building block used in multitude of differentially private data analysis algorithms.
Multitask Spectral Learning of Weighted Automata
We first present a natural notion of relatedness between WFAs by considering to which extent several WFAs can share a common underlying representation.
Spectral Learning from a Single Trajectory under Finite-State Policies
We present spectral methods of moments for learning sequential models from a single trajectory, in stark contrast with the classical literature that assumes the availability of multiple i. i. d.
Generalization Bounds for Weighted Automata
We present new data-dependent generalization guarantees for learning weighted automata expressed in terms of the Rademacher complexity of these families.
Differentially Private Policy Evaluation
We present the first differentially private algorithms for reinforcement learning, which apply to the task of evaluating a fixed policy.
Low-Rank Approximation of Weighted Tree Automata
We describe a technique to minimize weighted tree automata (WTA), a powerful formalisms that subsumes probabilistic context-free grammars (PCFGs) and latent-variable PCFGs.
