A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

Abstract

Over the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, and technology platforms in a wide range of applications, such as in smart healthcare, financial, political, and surveillance systems. In such applications, a large amount of data is generated about diverse aspects of our life. Although utilizing AI in real-world applications provides numerous opportunities for societies and industries, it raises concerns regarding data privacy. Data used in an AI system are cleaned, integrated, and processed throughout the AI life cycle. Each of these stages can introduce unique threats to individual's privacy and have an impact on ethical processing and protection of data. In this paper, we examine privacy risks in different phases of the AI life cycle and review the existing privacy-enhancing solutions. We introduce four different categories of privacy risk, including (i) risk of identification, (ii) risk of making an inaccurate decision, (iii) risk of non-transparency in AI systems, and (iv) risk of non-compliance with privacy regulations and best practices. We then examined the potential privacy risks in each AI life cycle phase, evaluated concerns, and reviewed privacy-enhancing technologies, requirements, and process solutions to countermeasure these risks. We also reviewed some of the existing privacy protection policies and the need for compliance with available privacy regulations in AI-based systems. The main contribution of this survey is examining privacy challenges and solutions, including technology, process, and privacy legislation in the entire AI life cycle. In each phase of the AI life cycle, open challenges have been identified.

Boundaries and Future Trends of ChatGPT Based on AI and Security Perspectives

“…Ensuring user privacy and minimizing security risks in the realm of large language models such as ChatGPT is a complex undertaking that necessitates the implementation of various techniques. These techniques encompass differential privacy, secure multi-party computation, privacy-aware machine learning algorithms, adversarial training, robustness testing, rate-limiting, blocking automated queries, anonymization, and encryption methods [35]. By employing these methods, it becomes possible to guarantee that user data remains appropriately safeguarded against unauthorized access and misuse throughout both the model training and interaction phases.…”