Secure Count Query on Encrypted Heterogeneous Data

Cost-effective and efficient sequencing technologies have resulted in massive genomic data availability. To compute on a large-scale genomic dataset, it is often required to outsource the dataset to the cloud. To protect data confidentiality, data owners encrypt sensitive data before outsourcing. Outsourcing enhances data owners to eliminate the storage management problem. Since genome data is large in volume, secure execution of researchers query is challenging. In this paper, we propose a method to securely perform count query on datasets containing genotype, phenotype, and numeric data. Our method modifies the prefix-tree proposed by Hasan et al. [1] to incorporate numerical data. The proposed method guarantees data privacy, output privacy, and query privacy. We preserve the security through encryption and garbled circuits. For a query of 100 single-nucleotide polymorphism (SNPs) sequence, we achieve query execution time approximately 3.5 minutes in a database of 1500 records. To the best of our knowledge, this is the first proposed secure framework that addresses heterogeneous biomedical data including numeric attributes.