PrivGenDB: Efficient and privacy-preserving query executions over encrypted SNP-Phenotype database

Privacy and security issues limit the query executions over genomics datasets, notably single nucleotide polymorphisms (SNPs), raised by sensitivity of this type data. Therefore, it is important to ensure that executing queries on these datasets do not reveal sensitive information, such as identity individuals their genetic traits, a data server. In paper, we propose present novel model, call PrivGenDB, confidentiality SNP-phenotype while queries. The in PrivGenDB enabled its system architecture search functionality provided searchable symmetric encryption (SSE). To best our knowledge, construction first SSE-based approach ensuring current approaches for genomic are limited only substring range sequence Besides, new encoding mechanism proposed incorporated model. This enables handle dataset containing both genotype phenotype also support storing managing other metadata, like gender ethnicity, privately. Furthermore, different queries, namely Count, Boolean, Negation k′-out-of-k match used analysis, supported executed PrivGenDB. execution efficient scalable biomedical research services. These demonstrated analytical empirical analysis presented paper. Specifically, studies with 5000 entries (records) 1000 SNPs demonstrate count/Boolean 40 take approximately 4.3s 86.4μs, respectively, outperforming existing schemes.