SCOTCH: Secure Counting Of encrypTed genomiC data using a Hybrid approach

Area of Science:

• Genomics
• Cloud Computing Security
• Bioinformatics

Background:

• Genomic data is large-scale and highly sensitive, requiring secure cloud analysis.
• Secure genotype counting is crucial for biomedical research applications like allele frequency computation.
• Existing secure outsourced data solutions lack efficiency for real-world applications.

Purpose of the Study:

• To develop an efficient and secure method for analyzing large-scale genomic data on untrusted public clouds.
• To enable secure delegation of computation and storage for genomic data owners.
• To improve the efficiency of secure genotype counting for downstream biomedical research.

Main Methods:

• A novel hybrid solution combining homomorphic encryption and Software Guard Extensions (SGX).
• Utilizing a rigorous theoretical model (homomorphic encryption) for privacy preservation.
• Leveraging hardware-based infrastructure (SGX) to accelerate computation.

Main Results:

• Demonstrated significant efficiency improvements in genomic data analysis.
• Successfully preserved the privacy of both data owners and data users.
• Validated the approach using real-world data from the Personal Genome Project.

Conclusions:

• The proposed hybrid solution effectively balances efficiency and privacy for cloud-based genomic data analysis.
• Homomorphic encryption and SGX integration offer a promising direction for secure bioinformatics.
• This method enhances the feasibility of large-scale genomic data analysis in public cloud environments.