Methods of privacy-preserving genomic sequencing data alignments

Area of Science:

• Bioinformatics
• Computational Biology
• Genomic Privacy

Background:

• Genomic data alignment is essential for mapping sequencing reads, database querying, and genetic testing.
• Increasing genome data necessitates robust privacy-preserving techniques for genomic sequencing data alignment.

Purpose of the Study:

• To provide a comprehensive review of secure genomic data comparison schemes.
• To analyze privacy threats, including adversaries and various attack types (inference, membership, identity tracing, completion).
• To classify and evaluate state-of-the-art privacy-preserving alignment methods across different scenarios.

Main Methods:

• Categorization of privacy attacks on genomic information.
• Classification of privacy-preserving alignment methods into three scenarios: large-scale reads mapping, encrypted genomic datasets querying, and genetic testing.
• Analysis of computational and communication complexity, and privacy requirements for existing methods.

Main Results:

• Identified and categorized key privacy threats and attacks in genomic data alignment.
• Classified current privacy-preserving alignment techniques based on application scenarios.
• Evaluated the trade-offs between privacy, computation, and communication for different approaches.

Conclusions:

• Privacy-preserving genomic data alignment is a critical and evolving field.
• Understanding current trends, significance, and challenges is vital for future research in secure genomic data handling.