Genomic privacy and security in the era of artificial intelligence and quantum computing

Area of Science:

• Genomics
• Bioinformatics
• Cybersecurity

Background:

• Advancements in sequencing technologies have led to a surge in publicly available genomic data.
• Increased data accessibility raises significant privacy and security concerns for genetic databases.
• Current data storage and sharing practices exhibit vulnerabilities to cyber-attacks and internal breaches.

Purpose of the Study:

• To review and analyze vulnerabilities in genomic data protection.
• To examine emerging threats, including AI-driven attacks and quantum computing risks.
• To explore machine learning methods for enhancing genomic data security and privacy.

Main Methods:

• Analysis of current genomic data storage and sharing vulnerabilities.
• Review of machine learning algorithms for data privacy (e.g., differential privacy, federated learning, Generative Adversarial Networks).
• Examination of AI-driven threats and quantum computing vulnerabilities.

Main Results:

• Progress has been made in mitigating common privacy breaches like re-identification and inference attacks.
• Persistent vulnerabilities remain, especially against advanced threats like model inversion and membership inference attacks.
• Machine learning methods show promise in balancing privacy preservation with data utility.

Conclusions:

• An integrated approach combining legislative frameworks and advanced technology is essential for genomic privacy.
• Further research is urgently needed to develop quantum-resistant cryptographic methods and blockchain-integrated security.
• Genomics researchers must prioritize data privacy and security for responsible data handling.