Cho et al. ‘describe a protocol for large-scale genome-wide analysis that facilitates quality control and population stratification correction […] while maintaining the confidentiality of underlying genotypes and phenotypes. […] This approach may help to make currently restricted data available to the scientific community and could potentially enable secure genome crowdsourcing, allowing individuals to contribute their genomes to a study without compromising their privacy.’
Gilad Asharov gave a presentation on privacy preserving search of similar patients in genomic data. The conference contribution was also authored by Shai Halevi, Yehuda Lindell and Tal Rabin. Both the video of the conference talk and the set of slides are available from IACR.
In this preprint, Bonte et al. describe both a homomorphic encryption approach and a secure multiparty computation approach and provide efficient implementations.
Jagadeesh et al. encode an individuals functional variants as a binary vector. They then use Yao’s protocol to identify relevant coincidences between pools of such vectors engaging in secure multiparty computation.
The authors ‘propose two different approximation methods to securely compute the edit distance among genomic sequences. [They] use shingling, private set intersection methods, the banded alignment algorithm, and garbled circuits to implement these methods.’
The authors ‘present a novel privacy-preserving algorithm for fully outsourcing the storage of large genomic data files to a public cloud and enabling researchers to efficiently search for variants of interest. In order to protect data and query confidentiality from possible leakage, [their] solution exploits optimal encoding for genomic variants and combines it with homomorphic encryption and private information retrieval.’
The authors ‘propose two lightweight algorithms (based on randomized response) which captures the efficacy while preserving the privacy of the participants in a genomic beacon service. [They] also elaborate the strength and weakness of the attack by explaining some of their statistical and mathematical models using real world genomic database.’
The authors ‘propose Fhe-Bloom and Phe-Bloom, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. Fhe-Bloom is fully secure in the semi-honest model while Phe-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance.’
The authors ‘present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing.’