In the era of Internet of Things (IoT), a large number of physical objects embedded with sensors and electronic devices exchange information through heterogeneous networks in various applications such as smart cities, electronic medical treatment and autonomous driving. The perception paradigm of Internet of everything has greatly changed our life bringing opportunities and challenges. Hundreds of millions of sensory devices continuously generate contextual data and exchange real-time analysis and decisions. Traditional cloud computing based on centralized storage is incapable of meeting the requirements of data-intensive services. Mobile edge computing (MEC) has been accepted as an effective solution to support data-driven services and local IoT applications, offloading computing, storage, and network resources from the cloud to the edge of the network.

Despite the advantages of MEC, such as high bandwidth, low latency, high efficiency, and scalability, it faces severe challenges in data security and privacy protection. IoT devices upload data to edge nodes in exchange for services, such as training models. Direct data uploads may jeopardize users' privacy, and malicious data tampering can mislead service decisions and even endanger the security of public services. Therefore, before upload, data needs to be protected using reliable security measures, such as homomorphic encryption, secure multiparty computing, differential privacy, and other encryption tools. Neural network is a widely used intelligent analysis tool. How to accurately and efficiently realize neural network reasoning and training over ciphertext is a critical challenge.

The aim of this Special Issue is to solicit original research articles highlighting the recent developments on security and privacy solutions for IoT and MEC. Review articles discussing the state of the art are also welcome.

Potential topics include but are not limited to the following:

Potential topics include but are not limited to the following:

• Efficient data encryption scheme for IoT and MEC
• Secure heterogeneous data aggregation and fusion scheme for IoT and MEC
• Secure data deduplication scheme for IoT and MEC
• Practical application of generating counter samples for IoT and MEC
• Secure data sharing and forwarding framework for IoT devices
• Secure and efficient client-to-edge collaborative computing for IoT and MEC
• Lightweight secure computing protocol design and formalize security analysis
• Privacy-preserving neural network reasoning of IoT and MEC
• Privacy-preserving deep learning training of IoT and MEC
• Secure, verifiable, personalized service scheme for IoT and MEC
• New comprehensive evaluation system for IoT and MEC
• Programmable security framework for privacy-preserving computing
• Reasonable communication and computing resource allocation strategies
• Attack and defense in neural network for IoT and MEC
• Future privacy challenges and solutions in IoT and MEC
• Federated learning and applications in IoT and MEC
• Block chain and applications in IoT and MEC