{"total":40,"rows":[{"id":"2303.00654","title":"How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy","created":"2023-03-01","cited_by_count":153,"area":null},{"id":"2303.15715","title":"Foundation Models and Fair Use","created":"2023-03-28","cited_by_count":116,"area":null},{"id":"2302.03098","title":"One-shot Empirical Privacy Estimation for Federated Learning","created":"2023-02-06","cited_by_count":36,"area":null},{"id":"2310.09130","title":"Split-and-Denoise: Protect large language model inference with local differential privacy","created":"2023-10-13","cited_by_count":32,"area":null},{"id":"2410.22235","title":"Auditing $f$-Differential Privacy in One Run","created":"2024-10-29","cited_by_count":30,"area":null},{"id":"2305.18447","title":"Unleashing the Power of Randomization in Auditing Differentially Private ML","created":"2023-05-29","cited_by_count":20,"area":null},{"id":"2309.17310","title":"Leave-one-out Distinguishability in Machine Learning","created":"2023-09-29","cited_by_count":20,"area":null},{"id":"2304.13850","title":"Do SSL Models Have D\\'ej\\`a Vu? A Case of Unintended Memorization in Self-supervised Learning","created":"2023-04-26","cited_by_count":19,"area":null},{"id":"2210.13662","title":"Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano","created":"2022-10-24","cited_by_count":17,"area":null},{"id":"2307.03928","title":"Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy","created":"2023-07-08","cited_by_count":15,"area":null},{"id":"2305.04146","title":"Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information","created":"2023-05-06","cited_by_count":13,"area":null},{"id":"2306.03235","title":"Information Flow Control in Machine Learning through Modular Model Architecture","created":"2023-06-05","cited_by_count":11,"area":null},{"id":"2307.00310","title":"Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD","created":"2023-07-01","cited_by_count":11,"area":null},{"id":"2211.03942","title":"Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design","created":"2022-11-08","cited_by_count":6,"area":null},{"id":"2309.13016","title":"Understanding Deep Gradient Leakage via Inversion Influence Functions","created":"2023-09-22","cited_by_count":6,"area":null},{"id":"2402.09478","title":"Data Reconstruction Attacks and Defenses: A Systematic Evaluation","created":"2024-02-13","cited_by_count":6,"area":null},{"id":"2403.02506","title":"Differentially Private Representation Learning via Image Captioning","created":"2024-03-04","cited_by_count":5,"area":null},{"id":"2509.07055","title":"Sequentially Auditing Differential Privacy","created":"2025-09-08","cited_by_count":5,"area":null},{"id":"2403.01268","title":"Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach","created":"2024-03-02","cited_by_count":4,"area":null},{"id":"2412.08544","title":"Training Data Reconstruction: Privacy due to Uncertainty?","created":"2024-12-11","cited_by_count":4,"area":null},{"id":"2209.10119","title":"Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information","created":"2022-09-21","cited_by_count":3,"area":null},{"id":"2207.02699","title":"Scaling Private Deep Learning with Low-Rank and Sparse Gradients","created":"2022-07-06","cited_by_count":2,"area":null},{"id":"2211.10173","title":"How Do Input Attributes Impact the Privacy Loss in Differential Privacy?","created":"2022-11-18","cited_by_count":2,"area":null},{"id":"2402.08344","title":"Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training","created":"2024-02-13","cited_by_count":2,"area":null},{"id":"2403.05598","title":"Privacy Amplification for the Gaussian Mechanism via Bounded Support","created":"2024-03-07","cited_by_count":2,"area":null},{"id":"2506.19836","title":"Machine Learning with Privacy for Protected Attributes","created":"2025-06-24","cited_by_count":2,"area":null},{"id":"2503.13872","title":"Empirical Calibration and Metric Differential Privacy in Language Models","created":"2025-03-18","cited_by_count":1,"area":null},{"id":"2510.07304","title":"Cocoon: A System Architecture for Differentially Private Training with Correlated Noises","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2303.16372","title":"On the Query Complexity of Training Data Reconstruction in Private Learning","created":"2023-03-29","cited_by_count":0,"area":null},{"id":"2309.00008","title":"Large-Scale Public Data Improves Differentially Private Image Generation Quality","created":"2023-08-04","cited_by_count":0,"area":null},{"id":"2408.12119","title":"Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective","created":"2024-08-22","cited_by_count":0,"area":null},{"id":"2412.06689","title":"Impact of Privacy Parameters on Deep Learning Models for Image Classification","created":"2024-12-09","cited_by_count":0,"area":null},{"id":"2502.05307","title":"Training Set Reconstruction from Differentially Private Forests: How Effective is DP?","created":"2025-02-07","cited_by_count":0,"area":null},{"id":"2507.18372","title":"On Reconstructing Training Data From Bayesian Posteriors and Trained Models","created":"2025-07-24","cited_by_count":0,"area":null},{"id":"2607.12354","title":"Reducing information dependency does not cause training data privacy. Adversarially non-robust features do","created":"2026-07-14","cited_by_count":0,"area":null},{"id":"2608.23382","title":"Spectrum-Aware Bounds on Invertibility for Privacy-Enhancing Instance Encoding","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2608.27782","title":"Memorization Is Not Extraction: Tight Differential-Privacy Bounds and Audit Blind Spots","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2609.37344","title":"A Sharp Transition in Data Reconstruction under Differential Privacy","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.02578","title":"High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.13768","title":"From Pixels to Privacy-Sensitive Features: An Information-Theoretic Framework for Reconstruction Bounds","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}