A High-Throughput Method to Detect Privacy-Sensitive Human Genomic Data

Finding the balance between privacy protection and data sharing is one of the main challenges in managing human genomic data nowadays. Novel privacy-enhancing technolo-gies are required to address the known disclosure threats to personal sensitive genomic data without precluding data sharing. In this paper, we propose a method that system-atically detects privacy-sensitive DNA segments coming di-rectly from an input stream, using as reference a knowl-edge database of known privacy-sensitive nucleic and amino acid sequences. We show that adding our detection method to standard security techniques provides a robust, efficient privacy-preserving solution that neutralizes threats related to recently published attacks on genome privacy based on short tandem repeats, disease-related genes, and genomic variations. Current global knowledge on human genomes demonstrates the feasibility of our approach to obtain a com-prehensive database immediately, which can also evolve au-tomatically to address future attacks as new privacy-sensitive sequences are identified. Additionally, we validate that the detection method can be fitted inline with the NGS—Next Generation Sequencing—production cycle by using Bloom filters and scaling out to faster sequencing machines