{"total":18,"rows":[{"id":"2102.12677","title":"Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning","created":"2021-02-25","cited_by_count":86,"area":null},{"id":"2203.00263","title":"Private Convex Optimization via Exponential Mechanism","created":"2022-03-01","cited_by_count":49,"area":null},{"id":"2207.00160","title":"When Does Differentially Private Learning Not Suffer in High Dimensions?","created":"2022-07-01","cited_by_count":48,"area":null},{"id":"2103.15352","title":"Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps","created":"2021-03-29","cited_by_count":21,"area":null},{"id":"2301.00457","title":"ReSQueing Parallel and Private Stochastic Convex Optimization","created":"2023-01-01","cited_by_count":17,"area":null},{"id":"2106.06127","title":"Differentially Private Federated Learning via Inexact ADMM","created":"2021-06-11","cited_by_count":11,"area":null},{"id":"2112.12998","title":"DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning","created":"2021-12-24","cited_by_count":8,"area":null},{"id":"2208.12708","title":"Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits","created":"2022-08-26","cited_by_count":8,"area":null},{"id":"2111.14838","title":"Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification","created":"2021-11-29","cited_by_count":6,"area":null},{"id":"2503.03486","title":"Differentially Private Learners for Heterogeneous Treatment Effects","created":"2025-03-05","cited_by_count":5,"area":null},{"id":"2503.21071","title":"Purifying Approximate Differential Privacy with Randomized Post-processing","created":"2025-03-27","cited_by_count":2,"area":null},{"id":"2411.16121","title":"DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing","created":"2024-11-25","cited_by_count":1,"area":null},{"id":"2511.08073","title":"Online Linear Regression with Paid Stochastic Features","created":"2025-11-11","cited_by_count":1,"area":null},{"id":"2605.05905","title":"Quadratic Objective Perturbation: Curvature-Based Differential Privacy","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2202.09409","title":"Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates","created":"2022-02-18","cited_by_count":0,"area":null},{"id":"2601.13698","title":"Does Privacy Always Harm Fairness? Data-Dependent Trade-offs via Chernoff Information Neural Estimation","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2601.17257","title":"A Constrained Optimization Perspective of Unrolled Transformers","created":"2026-01-24","cited_by_count":0,"area":null},{"id":"2605.20521","title":"An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees","created":"2026-05-19","cited_by_count":0,"area":null}],"next_cursor":null}