{"total":33,"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":"2203.15205","title":"SPAct: Self-supervised Privacy Preservation for Action Recognition","created":"2022-03-29","cited_by_count":47,"area":null},{"id":"2305.05391","title":"Privacy-preserving Adversarial Facial Features","created":"2023-05-08","cited_by_count":24,"area":null},{"id":"2112.12785","title":"NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning","created":"2021-12-23","cited_by_count":21,"area":null},{"id":"2104.01753","title":"Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics","created":"2021-04-05","cited_by_count":20,"area":null},{"id":"2309.13038","title":"Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?","created":"2023-09-22","cited_by_count":13,"area":null},{"id":"2109.03386","title":"On Characterizing the Trade-off in Invariant Representation Learning","created":"2021-09-08","cited_by_count":12,"area":null},{"id":"2308.11223","title":"LDP-Feat: Image Features with Local Differential Privacy","created":"2023-08-22","cited_by_count":10,"area":null},{"id":"2307.10981","title":"PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks","created":"2023-07-20","cited_by_count":9,"area":null},{"id":"2110.00740","title":"FICGAN: Facial Identity Controllable GAN for De-identification","created":"2021-10-02","cited_by_count":6,"area":null},{"id":"2503.00383","title":"Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems","created":"2025-03-01","cited_by_count":6,"area":null},{"id":"2205.01950","title":"Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation","created":"2022-05-04","cited_by_count":5,"area":null},{"id":"2408.00950","title":"PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services","created":"2024-08-01","cited_by_count":4,"area":null},{"id":"2201.12406","title":"Syfer: Neural Obfuscation for Private Data Release","created":"2022-01-28","cited_by_count":3,"area":null},{"id":"2308.13513","title":"Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs","created":"2023-08-25","cited_by_count":3,"area":null},{"id":"2410.17098","title":"Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2503.02132","title":"Video-DPRP: A Differentially Private Approach for Visual Privacy-Preserving Video Human Activity Recognition","created":"2025-03-03","cited_by_count":3,"area":null},{"id":"2302.14473","title":"Implicit Bilevel Optimization: Differentiating through Bilevel Optimization Programming","created":"2023-02-28","cited_by_count":2,"area":null},{"id":"2309.04427","title":"Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach","created":"2023-09-08","cited_by_count":2,"area":null},{"id":"2401.07205","title":"Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models","created":"2024-01-14","cited_by_count":2,"area":null},{"id":"2404.05047","title":"Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4","created":"2024-04-07","cited_by_count":2,"area":null},{"id":"2409.02715","title":"Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approach","created":"2024-09-01","cited_by_count":2,"area":null},{"id":"2204.09962","title":"ChildPredictor: A Child Face Prediction Framework with Disentangled Learning","created":"2022-04-21","cited_by_count":1,"area":null},{"id":"2205.09624","title":"Focused Adversarial Attacks","created":"2022-05-19","cited_by_count":1,"area":null},{"id":"2506.19886","title":"Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2203.12192","title":"Learning to Censor by Noisy Sampling","created":"2022-03-23","cited_by_count":0,"area":null},{"id":"2203.14299","title":"Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework","created":"2022-03-27","cited_by_count":0,"area":null},{"id":"2305.07815","title":"MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning","created":"2023-05-13","cited_by_count":0,"area":null},{"id":"2310.15590","title":"Facial Data Minimization: Shallow Model as Your Privacy Filter","created":"2023-10-24","cited_by_count":0,"area":null},{"id":"2404.05043","title":"Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization","created":"2024-04-07","cited_by_count":0,"area":null},{"id":"2507.10194","title":"Learning Private Representations through Entropy-based Adversarial Training","created":"2025-07-14","cited_by_count":0,"area":null},{"id":"2603.15842","title":"Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning","created":"2026-03-16","cited_by_count":0,"area":null},{"id":"2610.00382","title":"On the Relationship between Model Quantization and Model Inversion Attacks","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}