The research advances of intelligent devices and seamless networking technologies have spawned the emergence of computing systems that are embedded in every aspect of human life. It benefits from developments from evolving computing paradigms, such as edge computing, federated learning, and digital networks, to diverse computing services, such as mobile healthcare, crowdsensing, and the metaverse. The implementations of these emerging computing systems involve data collection and knowledge extraction of users’ daily lives, including smartphones (accessories) that record location, fitness, and other contextual information, and network traffic that conveys sharing behaviors, access records, and application usage patterns. Abusing and disclosing such sensitive information, either implicitly or explicitly, causes significant security concerns and privacy breaches, which go against both user interests and regulations.

To meet users’ privacy requirements in terms of various legislations, Privacy Enhancing Technologies (PETs) are widely investigated and gain expanding attention. PETs explore the joint efforts of homomorphic encryption, secure multi-party computation, differential privacy, and other techniques for building privacy-preserving algorithms, applications, systems, and services. Yet, the emerging computing scenarios exhibit a larger attack surface, prone to new forms of privacy concerns and vulnerabilities, which necessitates insights towards privacy assessment, dedicated solutions, and general design guidelines.

In this Special Issue, we welcome submissions exposing and addressing the underlying privacy issues in emerging computing systems and aim to publish papers presenting recent research results and implementation experiences from academia, industrial communities, and governments. Papers offering a perspective on related work and identifying promising directions are also encouraged. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Privacy and PETs in in-network computing
• Privacy and PETs in digital networks
• Privacy and PETs in federal learning
• PETs for crowdsourcing and crowdsensing
• Privacy and PETs in the metaverse
• Artificial intelligence for PETs in emerging computing paradigms
• Advanced cryptography protocols and algorithms for privacy
• Privacy-preserving big data analytics
• Privacy risk assessment and visualization
• Implementation and adaptive configuration of PETs