Asian Journal of Research in Computer Science

Aims: This systematic review aims to explore how artificial intelligence (AI) enhances privacy-preserving techniques in database systems, focusing on anonymization, differential privacy, and secure multi-party computation (SMPC), while evaluating their effectiveness in balancing privacy and data utility and identifying implementation challenges.

Methodology: A comprehensive search strategy was applied using predefined search strings targeting AI-driven anonymization, differential privacy, and SMPC in database systems. The initial search yielded 62 records, which were screened based on inclusion criteria (peer-reviewed studies published in English between 2020 and 2025, focusing on AI-enhanced privacy-preserving techniques in databases) and exclusion criteria (non-peer-reviewed sources, studies lacking empirical results or database focus). After screening and eligibility assessment, 20 studies were included. Data extraction focused on sub-themes, AI enhancements, application domains, challenges, and effectiveness metrics, followed by qualitative thematic synthesis to address the research questions.

Results: Of the 20 included studies, AI-driven anonymization reduced information loss by up to 12% in accuracy improvements using blockchain schemes and lowered execution times, while clustering methods enhanced privacy in social networks. Differential privacy preserved 60.81% data originality while reducing privacy risks by 20.05% in hybrid models. SMPC enabled secure genomic data exploration, with fast Machine learning training (<45 seconds for binary classifiers), and processed 10,000 variables across 20 parties in under 5 minutes using no-code tools. Challenges included scalability issues and privacy-utility trade-offs like excessive noise in biomedical databases.

Conclusion: AI significantly enhances privacy-preserving techniques in databases, enabling effective privacy protection with practical utility across healthcare and social networks. However, challenges like scalability and privacy-utility trade-offs highlight the need for future research into combined methods and standardized evaluation frameworks to ensure reliable, widespread adoption in database systems.