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1. Security Risk and Attacks in AI: A Survey of Security and Privacy

   https://doi.org/10.1109/COMPSAC57700.2023.00284  

   Rahman, Md Mostafizur; Siddika Arshi, Aiasha; Hasan, Md Mehedi; Farzana Mishu, Sumayia; Shahriar, Hossain; Wu, Fan (June 2023, 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC))

   This survey paper provides an overview of the current state of Artificial Intelligence (AI) attacks and risks for AI security and privacy as artificial intelligence becomes more prevalent in various applications and services. The risks associated with AI attacks and security breaches are becoming increasingly apparent and cause many financial and social losses. This paper will categorize the different types of attacks on AI models, including adversarial attacks, model inversion attacks, poisoning attacks, data poisoning attacks, data extraction attacks, and membership inference attacks. The paper also emphasizes the importance of developing secure and robust AI models to ensure the privacy and security of sensitive data. Through a systematic literature review, this survey paper comprehensively analyzes the current state of AI attacks and risks for AI security and privacy and detection techniques. 

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2. A survey of security and privacy issues of machine unlearning

   https://doi.org/10.1002/aaai.12209  

   Chen, Aobo; Li, Yangyi; Zhao, Chenxu; Huai, Mengdi (January 2025, AI Magazine)

   Abstract Machine unlearning is a cutting‐edge technology that embodies the privacy legal principle of the right to be forgotten within the realm of machine learning (ML). It aims to remove specific data or knowledge from trained models without retraining from scratch and has gained significant attention in the field of artificial intelligence in recent years. However, the development of machine unlearning research is associated with inherent vulnerabilities and threats, posing significant challenges for researchers and practitioners. In this article, we provide the first comprehensive survey of security and privacy issues associated with machine unlearning by providing a systematic classification across different levels and criteria. Specifically, we begin by investigating unlearning‐based security attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning. Additionally, we explore existing countermeasures and mitigation strategies designed to protect models from malicious unlearning‐based attacks targeting both security and privacy. Further, we provide a detailed comparison between machine unlearning‐based security and privacy attacks and traditional malicious attacks. Finally, we discuss promising future research directions for security and privacy issues posed by machine unlearning, offering insights into potential solutions and advancements in this evolving field. 

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3. Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance

   https://doi.org/10.1145/3645102  

   Wei, Wenqi; Liu, Ling (February 2024, ACM Computing Surveys)

   Emerging Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact. However, recent studies have identified new attack surfaces and risks caused by security, privacy, and fairness issues in AI systems. In this paper, we review representative techniques, algorithms, and theoretical foundations for trustworthy distributed AI through robustness guarantee, privacy protection, and fairness awareness in distributed learning. We first provide a brief overview of alternative architectures for distributed learning, discuss inherent vulnerabilities for security, privacy, and fairness of AI algorithms in distributed learning, and analyze why these problems are present in distributed learning regardless of specific architectures. Then we provide a unique taxonomy of countermeasures for trustworthy distributed AI, covering (1) robustness to evasion attacks and irregular queries at inference, and robustness to poisoning attacks, Byzantine attacks, and irregular data distribution during training; (2) privacy protection during distributed learning and model inference at deployment; and (3) AI fairness and governance with respect to both data and models. We conclude with a discussion on open challenges and future research directions toward trustworthy distributed AI, such as the need for trustworthy AI policy guidelines, the AI responsibility-utility co-design, and incentives and compliance. 

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4. Artificial Intelligence Assistants: Convenient vs. Security and Privacy

   Collins, I.; Boonthum-Denecke, C. (January 2021, ADMI 2021: The Symposium of Computing at Minority Institutions)

   Artificial Intelligence, intelligence demonstrated by machines, has emerged as one of the most convenient and personable applications of everyday life. Specifically, AI powers digital personal assistants to answer user questions and automate everyday tasks. AI Assistants listen continuously to answer the user, even when not in use. Why is this a problem? For a hacker, this makes any digital assistant a potential listening device, a major security and privacy issue. While some companies are handling this situation well, others are falling behind as their AI components are slowly dying in the consumer market. Which digital assistant is best and most secure you may ask? This paper will first detail how each AI assistant works from a technical perspective. Then based on survey results, this paper will detail how AI Assistants rank in terms of overall security and performance 

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5. Security, Privacy, and Access Control in Information-Centric Networking: A Survey

   https://doi.org/10.1109/COMST.2017.2749508  

   Tourani, Reza; Misra, Satyajayant; Mick, Travis; Panwar, Gaurav (January 2018, IEEE Communications surveys and tutorials)

   Information-centric networking (ICN) replaces the widely used host-centric networking paradigm in communication networks (e.g., Internet and mobile ad hoc networks) with an information-centric paradigm, which prioritizes the delivery of named content, oblivious of the contents' origin. Content and client security, provenance, and identity privacy are intrinsic by design in the ICN paradigm as opposed to the current host centric paradigm where they have been instrumented as an afterthought. However, given its nascency, the ICN paradigm has several open security and privacy concerns. In this paper, we survey the existing literature in security and privacy in ICN and present open questions. More specifically, we explore three broad areas: 1) security threats; 2) privacy risks; and 3) access control enforcement mechanisms. We present the underlying principle of the existing works, discuss the drawbacks of the proposed approaches, and explore potential future research directions. In security, we review attack scenarios, such as denial of service, cache pollution, and content poisoning. In privacy, we discuss user privacy and anonymity, name and signature privacy, and content privacy. ICN's feature of ubiquitous caching introduces a major challenge for access control enforcement that requires special attention. We review existing access control mechanisms including encryption-based, attribute-based, session-based, and proxy re-encryption-based access control schemes. We conclude the survey with lessons learned and scope for future work. 

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