A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
Authors: Alberto Blanco-Justicia, David Sanchez, Josep Domingo-Ferrer, Krishnamurty Muralidhar | Published: 2022-06-09 | Updated: 2022-07-05
プライバシー評価
Analytical Composition of Differential Privacy via the Edgeworth Accountant
Authors: Hua Wang, Sheng Gao, Huanyu Zhang, Milan Shen, Weijie J. Su | Published: 2022-06-09
Rate Distortion Tradeoff in Private Read Update Write in Federated Submodel Learning
Authors: Sajani Vithana, Sennur Ulukus | Published: 2022-06-07
Group privacy for personalized federated learning
Authors: Filippo Galli, Sayan Biswas, Kangsoo Jung, Tommaso Cucinotta, Catuscia Palamidessi | Published: 2022-06-07 | Updated: 2022-09-04
Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?
Authors: Huiyu Li, Nicholas Ayache, Hervé Delingette | Published: 2022-06-07
Defense Against Gradient Leakage Attacks via Learning to Obscure Data
Authors: Yuxuan Wan, Han Xu, Xiaorui Liu, Jie Ren, Wenqi Fan, Jiliang Tang | Published: 2022-06-01
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data
Authors: Huancheng Chen, Haris Vikalo | Published: 2022-06-01 | Updated: 2023-04-20
Differentially Private Shapley Values for Data Evaluation
Authors: Lauren Watson, Rayna Andreeva, Hao-Tsung Yang, Rik Sarkar | Published: 2022-06-01
Privacy for Free: How does Dataset Condensation Help Privacy?
Authors: Tian Dong, Bo Zhao, Lingjuan Lyu | Published: 2022-06-01
Private Federated Submodel Learning with Sparsification
Authors: Sajani Vithana, Sennur Ulukus | Published: 2022-05-31