Privacy preservation in federated learning: An insightful survey from the GDPR perspective

Federated Learning: Challenges, Methods, and Future Directions

(2020) Tian Li et al.   IEEE SIGNAL PROCESSING MAGAZINE

On the responsible use of digital data to tackle the COVID-19 pandemic

(2020) Marcello Ienca et al.   NATURE MEDICINE

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

(2019) Shiqiang Wang et al.   IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS

Demystifying Parallel and Distributed Deep Learning

(2019) Tal Ben-Nun et al.   ACM COMPUTING SURVEYS

Privacy-Preserving Deep Learning via Additively Homomorphic Encryption

(2018) Le Trieu Phong et al.   IEEE Transactions on Information Forensics and Security

A Survey on Homomorphic Encryption Schemes

(2018) Abbas Acar et al.   ACM COMPUTING SURVEYS

Weighing the impact of GDPR

(2018) Samuel Greengard   COMMUNICATIONS OF THE ACM

Exposed! A Survey of Attacks on Private Data

(2017) Cynthia Dwork et al.   Annual Review of Statistics and Its Application

Fast Cut-and-Choose-Based Protocols for Malicious and Covert Adversaries

(2015) Yehuda Lindell   JOURNAL OF CRYPTOLOGY

Data, privacy, and the greater good

(2015) E. Horvitz et al.   SCIENCE

Big Data Deep Learning: Challenges and Perspectives

(2014) Xue-Wen Chen et al.   IEEE Access

Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data

(2013) Anand D. Sarwate et al.   IEEE SIGNAL PROCESSING MAGAZINE

Privacy-preserving data publishing

(2010) Benjamin C. M. Fung et al.   ACM COMPUTING SURVEYS

Computing arbitrary functions of encrypted data

(2010) Craig Gentry   COMMUNICATIONS OF THE ACM