Recent Advances in the Technologies and Applications of Privacy-Preserving Computing

Dear Colleagues,

With the rapid development of cloud computing, Internet of Things, sensor networks, mobile Internet, etc., the era of big data has brought the problem of data privacy protection as well as computing results. Privacy-preserving computing is envisioned as an effective way to perform the analysis and calculation of data without disclosing data privacy. The key technologies of privacy-preserving computing, such as cryptographic primitives, federated learning, and differential privacy, have become a very active research area in addressing privacy and security issues in emerging applications. Although existing research results have shown significant progress toward privacy-preserving computing technologies and applications, numerous research challenges remain to be addressed. This Special Issue aims to collect high-quality articles focusing on the latest advances in privacy-preserving computing technologies and their applications, including theories, technologies, and emerging applications. This Special Issue hopes to allow researchers to present the new developments and discuss the future applications of this field. Topics include: cryptography technologies; privacy-preserving technologies in cloud/edge/fog computing; privacy-preserving technologies in Internet of Things, social networks, and sensor networks; privacy-preserving technologies in data analysis, databases, intelligent medical service, artificial intelligence, and machine learning; privacy-preserving technologies in blockchain systems; privacy-preserving technologies in smart city, smart grid, and intelligent transportation systems; privacy-preserving technologies in supply chain, logistics, digital finance, education, healthcare, entertainment, and sustainable manufacturing; threat and vulnerability analysis of privacy-preserving technologies; federated learning; and differential privacy.

Dr. Yujue Wang
Dr. Hua Deng
Dr. Haibin Zheng
Guest Editors

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