When Machine Learning Meets Privacy: A Survey and Outlook
University of Technology Sydney（悉尼科技大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织 Data61）；The University of Sydney（悉尼大学）；La Trobe University（乐卓博大学）；The University of Melbourne（墨尔本大学）
Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, Zihuai Lin
The newly emerged machine learning (e.g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning (ML) is still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This paper surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.
When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；College of Mathematics and Computer Science, Fuzhou University（福州大学数学与计算机科学学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence
School of Informatics, Xiamen University, China（厦门大学信息学院，中国）；Internet of Things Thrust, Hong Kong University of Science and Technology (Guangzhou), China（香港科技大学（广州）物联网研究组，中国）
FedPoP: Federated Learning Meets Proof of Participation
IMDEA Networks Institute（IMDEA网络研究所）；Universidad Carlos III de Madrid（卡洛斯三世大学）；University of California, Irvine（加州大学尔湾分校）；Stealth Software Technologies Inc.（隐形软件技术公司）
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
Department of Computer Science and Engineering, BITS Pilani Dubai Campus（计算机科学与工程系，比斯帕尼迪拜校区）；Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科技大学）
