Security and Privacy Evaluation of Machine Learning in Networks

Dear Colleagues,

Machine learning algorithms have been playing an increasingly important role in many practical computing systems, such as auto-piloting, medicine diagnosis, spam detection, and person reidentification. Meanwhile, such machine learning algorithms are usually confronted with critical threats in an open and adversarial setting. These threats include evasion attack, backdoor attack, model stealth, data leakage and so on, which significantly affect the effectiveness and security of the machine learning systems.

To better protect the security of machine learning algorithms, it is necessary to evaluate multiple aspects of their security risks. In this regard, robust and certificated evaluation methods are critically needed in various industrial and academic communities. Although recent studies have provided many insightful works in this field (e.g., empirical model robustness evaluation), comprehensive and sophisticated security-oriented evaluations for machine learning algorithms remain rarely explored.

This feature topic will benefit the research community towards identifying challenges and disseminating the latest methodologies and solutions regarding security and privacy evaluation issues in machine learning. The ultimate objective is to publish high-quality articles presenting open issues, delivering algorithms, protocols, frameworks, and solutions for security evaluation in machine learning. Relevant topics include, but are not limited to, the following:

• Adversarial machine learning;
• Model robustness boosting and evaluation;
• Secure federated machine learning;
• Secure neural network inference;
• Certificated evaluation intelligent systems;
• Attack and defense for machine learning systems;
• Semi-supervised adversarial learning;
• Hardware/software co-design data security;
• Verification mechanism for neural networks;
• Security evaluation of generative models;
• Machine-learning-based security and privacy design;
• Security protocols for communication networks;
• Information-theoretical foundations for advanced security and privacy techniques;
• Encryption and decryption algorithms for machine learning systems networks;
• Security and privacy design for intelligent vehicle networks;
• Blockchain-based solutions for communication networks;
• Anonymity in data transmission;
• Prototype and testbed for security and privacy solutions;
• Challenges of security and privacy in node–edge–cloud computation.

Dr. Xianmin Wang
Dr. Jing Li
Prof. Dr. Di Wu
Dr. Mingliang Zhou
Guest Editors

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• adversarial machine learning
• security evaluation
• certificated evaluation
• privacy-preserving
• federated machine learning
• robustness of machine learning
• blockchain