Multiparty computation is a form of trustworthy computation approach in which two or more parties jointly evaluate a function and obtain its outcome without accessing the private inputs of the other parties. Cryptography is the classic enabler of Secure Multiparty Computing (SMC), but most real-time and online computations are incompatible with cryptographic activities. Trusted Execution Environments (TEEs) are promising alternatives for making SMC more tractable on any network, since they require hardware enforcement, isolation of code, and data. The emergence of a variety of trusted execution environments in recent years has created an opportunity to offload some security and privacy costs associated with widespread multiparty computation.

Several adversaries are trying to violate the safety and privacy of SMC, and they can be categorized as semihonest, malicious, or covert. Recently, data analytics has co-operated with multiparty communications in various fields such as agriculture, bioinformatics, environmental science, data analytics, and so on. Wherever network communication happens, it is necessary to ensure the co-operation of security and privacy. Artificial Intelligence (AI) can be used to identify spam, viruses, data theft, and improved security are possible in pervasive communication networks. Data breaching, data integrity, and data collusion are all key targets for cybersecurity and privacy attacks on IoT devices and their data. Privacy considerations arise naturally when data is transported to and from the cloud. Indeed, privacy solutions must meet ethical and legal criteria while not impeding viable business models, whereas privacy-preserving solutions are unlikely to become feasible and effective without new rules. Furthermore, if the data is leaked and not properly encrypted, it compromises the user's privacy for nonauthorized virtual profiling.

This Special Issue is to collate original research and review articles describing advances in security awareness and privacy preferences for multiparty communications and computation, as well as legal and economic challenges, solutions in various IoT application domains, and so on.

Potential topics include but are not limited to the following:

• Security and privacy modeling in the cloud-based system
• Security/privacy/trust issues in network architectures
• Attack prediction, prevention, and co-operation using machine learning algorithms
• Multiparty trust model for the IoT
• Multitenancy related security/privacy issues
• Vulnerabilities in network infrastructure
• Co-operative communication techniques for the IoT
• Anonymization techniques in cybersecurity
• Federated learning in digital healthcare using IoT
• Secure architecture for smart cities using IoT
• Lightweight cryptography protocols
• Security testing, ethical hacking, and security certifications
• Privacy-preserving pervasive computing techniques
• Intelligent and secure transportation systems
• Cyber threat intelligence model for the cloud-based system