Visible to the public Secure Count Query on Encrypted Heterogeneous Data

TitleSecure Count Query on Encrypted Heterogeneous Data
Publication TypeConference Paper
Year of Publication2020
AuthorsRahman Mahdi, Md Safiur, Sadat, Md Nazmus, Mohammed, Noman, Jiang, Xiaoqian
Conference Name2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Date Publishedaug
Keywordsapplied cryptography in data security, Bioinformatics, data privacy, Encryption, genomics, heterogeneous data., outsourcing, secure cloud computing, Sequential analysis, storage management
AbstractCost-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.
DOI10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00098
Citation Keyrahman_mahdi_secure_2020