Secure Similar Sequence Query on Outsourced Genomic Data

Abstract

The growing availability of genomic data is unlocking research potentials on genomic-data analysis. It is of great importance to outsource the genomic-analysis tasks onto clouds to leverage their powerful computational resources over the large-scale genomic sequences. However, the remote placement of the data raises personalprivacy concerns, and it is challenging to evaluate data-analysis functions on outsourced genomic data securely and efficiently. In this work, we study the secure similar-sequence-query (SSQ) problem over outsourced genomic data, which has not been fully investigated. To address the challenges of security and efficiency, we propose two protocols in the mixed form, which combine twoparty secure secret sharing, garbled circuit, and partial homomorphic encryptions together and use them to jointly fulfill the secure SSQ function. In addition, our protocols support multiuser queries over a joint genomic data set collected from multiple data owners, making our solution scalable. We formally prove the security of protocols under the semi-honest adversary model, and theoretically analyze the performance. We use extensive experiments over real-world dataset on a commercial cloud platform to validate the efficacy of our proposed solution, and demonstrate the performance improvements compared with state-of-the-art works.

Towards Efficient Privacy-Preserving Similar Sequence Queries on Outsourced Genomic Databases

“…Cheng et al [8]. The most recent related work that covers privacypreserving ED computations in the outsourcing scenario is [8]. Unfortunately, the authors give the exact run-times only for the ED computation between two sequences.…”

“…Unfortunately, the authors give the exact run-times only for the ED computation between two sequences. We compare our ED runtimes in the system setting of [8], i.e., the outsourcing scenario, with the run-times of their most efficient protocol in Table 2.…”

“…Furthermore, we enhance our algorithms to use Single Instruction Multiple Data (SIMD) operations which allows parallelization. Our implementation on commodity hardware yields more than a factor of 20 000× improvement over [8]. Even compared to the highly efficient two-party protocol of [3], our run-times are by factor 5-6× faster than theirs in the same two-party setting.…”

“…The authors of [8] design an algorithm for privacy-preserving SSQ based on the ED approximation of [3] using homomorphic encryption in an outsourcing scenario with two non-colluding, semi-honest parties. Our algorithm significantly outperforms theirs in the same setting, which we extensively discuss in Section 5.…”

“…However, the cost for OTs remain approximately the same as in the protocol of [3] for large databases (less than 1 % overhead for a database with 10 000 sequences). In contrast to [8], we compute most of the functionalities using generic protocols which enables arbitrary extensions of our protocol.…”