{"total":15,"rows":[{"id":"2207.00099","title":"Measuring Forgetting of Memorized Training Examples","created":"2022-06-30","cited_by_count":118,"area":null},{"id":"2410.22235","title":"Auditing $f$-Differential Privacy in One Run","created":"2024-10-29","cited_by_count":30,"area":null},{"id":"2210.02912","title":"CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning","created":"2022-10-06","cited_by_count":24,"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":"2402.09540","title":"Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?","created":"2024-02-14","cited_by_count":12,"area":null},{"id":"2304.07927","title":"A Randomized Approach for Tight Privacy Accounting","created":"2023-04-17","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":"2310.20579","title":"Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks","created":"2023-10-31","cited_by_count":10,"area":null},{"id":"2407.02191","title":"Attack-Aware Noise Calibration for Differential Privacy","created":"2024-07-02","cited_by_count":8,"area":null},{"id":"2302.00766","title":"Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds","created":"2023-02-01","cited_by_count":2,"area":null},{"id":"2402.14397","title":"Closed-Form Bounds for DP-SGD against Record-level Inference","created":"2024-02-22","cited_by_count":2,"area":null},{"id":"2210.13028","title":"Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens","created":"2022-10-24","cited_by_count":0,"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":"2605.23879","title":"On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy","created":"2026-05-22","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}],"next_cursor":null}