Supervised learning models have been increasingly used for making decisions about individuals in applications such as hiring, lending, and college admission. These models may inherit pre-existing biases from training datasets and discriminate against protected attributes (e.g., race or gender). In addition to unfairness, privacy concerns also arise when the use of models reveals sensitive personal information. Among various privacy notions, differential privacy has become popular in recent years. In this work, we study the possibility of using a differentially private exponential mechanism as a post-processing step to improve both fairness and privacy of supervised learning models. Unlike many existing works, we consider a scenario where a supervised model is used to select a limited number of applicants as the number of available positions is limited. This assumption is well-suited for various scenarios, such as job application and college admission. We use ``equal opportunity'' as the fairness notion and show that the exponential mechanisms can make the decision-making process perfectly fair. Moreover, the experiments on real-world datasets show that the exponential mechanism can improve both privacy and fairness, with a slight decrease in accuracy compared to the model without post-processing.
FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility
The School of Computer Science, Shanghai Jiao Tong University, Shanghai, China（上海交通大学计算机科学学院，上海，中国）；The Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China（上海信息安全集成管理技术重点实验室，上海，中国）；The School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳）科学与工程学院，中国）
SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI
Lucy Family Institute for Data & Society（数据与社会卢西家族研究所）；University of Notre Dame（北德克萨斯大学）；Department of Applied and Computational Mathematics and Statistics（应用与计算数学与统计系）
Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃朗根-纽伦堡弗里德里希-亚历山大大学模式识别实验室）；Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen（亚琛大学诊断与介入放射科）；Department of Radiology, Stanford University（斯坦福大学放射科）；Department of Urology, Stanford University（斯坦福大学泌尿科）
FairDP: Certified Fairness with Differential Privacy
New Jersey Institute of Technology（新泽西理工学院）；University of Virginia（弗吉尼亚大学）；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）；University of Florida（佛罗里达大学）；University of Pennsylvania（宾夕法尼亚大学）
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
National Engineering Laboratory for Big Data Analytics (NEL-BDA)（大数据分析国家工程实验室）；School of Mathematics and Statistics（数学与统计学学院）；Ministry of Education Key Lab for Intelligent Networks and Network Security（教育部智能网络与网络安全重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning
Beijing University of Posts and Telecommunications（北京邮电大学）；Macquarie University（麦考瑞大学）；Peng Cheng Laboratory（鹏城实验室）；University of Technology Sydney（悉尼大学）；University of Manitoba（曼尼托巴大学）
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
INRIA（法国国家信息与自动化研究所）；École Polytechnique（巴黎综合理工学院）；IPP（IPP机构）；Mathematical Institute of the Serbian Academy of Sciences and Arts（塞尔维亚科学院数学研究所）；Inria Centre at the University Grenoble Alpes（格勒诺布尔大学Inria中心）
On the Impact of Multi-dimensional Local Differential Privacy on Fairness
Inria（法国国家信息与自动化研究所）；École Polytechnique (IPP)（巴黎综合理工学院）；Inria Centre at the University Grenoble Alpes（格勒诺布尔阿尔卑斯大学法国国家信息与自动化研究所中心）；College of Computer Engineering and Science, Prince Mohammad Bin Fahd University（穆罕默德·本·法赫德王子大学计算机工程与科学学院）
Achieving Hilbert-Schmidt Independence Under Rényi Differential Privacy for Fair and Private Data Generation
Department of Mathematics and Computer Science University of Southern Denmark（丹麦南部大学数学与计算机科学系）；Department of Mathematics and Computer Science Freie Universität Berlin（柏林自由大学数学与计算机科学系）；Faculty for Informatik Universität der Bundeswehr München（联邦国防军大学慕尼黑信息学院）
Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface
Bangladesh University of Business and Technology (BUBT)（孟加拉国商业与技术大学（BUBT））；Jagannath University（贾格纳特大学）；University of New South Wales (UNSW)（新南威尔士大学（UNSW））
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（信息与通信工程学院，北京邮电大学）；School of Computing, Macquarie University（计算学院，麦考瑞大学）；Department of Broadband Communication, Peng Cheng Laboratory（宽带通信部，鹏城实验室）；China Telecom, Sichuan Branch（中国电信四川分公司）
FAIRPLAI: A Human-in-the-Loop Approach to Fair and Private Machine Learning
Dept. of Computer Science, The University of Texas at El Paso（得克萨斯大学埃尔帕索分校计算机科学系）；Dept. of Mathematics, Mountain View High School（山景高中数学系）；Dept. of Mathematics, El Paso Independent School District（埃尔帕索独立学区数学系）
Towards Fair and Privacy-Preserving Federated Deep Models
National University of Singapore（新加坡国立大学）；Monash University（莫纳什大学）；IBM Singapore Lab（IBM新加坡实验室）；The University of Melbourne（墨尔本大学）；Swinburne University of Technology（斯威本科技大学）；Nanyang Technological University（南洋理工大学）；Australian National University（澳大利亚国立大学）
