PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension

Chen, Feng; Wang, Chenghong; Dai, Wenrui; Jiang, Xiaoqian; Mohammed, Noman; Aziz, Momin Al; Sadat, Nazmus; Sahinalp, Cenk; Lauter, Kristin; Wang, Shuang

doi:10.1186/s12920-017-0281-2

Cited by 40 publications

(32 citation statements)

References 43 publications

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“…For example, the solutions will have accessible interfaces (along with installation and user manuals) that allow integration into existing data-sharing portals (e.g., secure Beacon services, public cloud, etc.). We will also design more challenging tasks to tackle more practical problems in biomedical research, such as performing machine model learning over encrypted data, and adopting hardware based solutions 22 , 43 , 44 to handle genomic data analysis at the whole genome scale.…”

Section: Discussionmentioning

confidence: 99%

A community effort to protect genomic data sharing, collaboration and outsourcing

et al. 2017

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The human genome can reveal sensitive information and is potentially re-identifiable, which raises privacy and security concerns about sharing such data on wide scales. In 2016, we organized the third Critical Assessment of Data Privacy and Protection competition as a community effort to bring together biomedical informaticists, computer privacy and security researchers, and scholars in ethical, legal, and social implications (ELSI) to assess the latest advances on privacy-preserving techniques for protecting human genomic data. Teams were asked to develop novel protection methods for emerging genome privacy challenges in three scenarios: Track (1) data sharing through the Beacon service of the Global Alliance for Genomics and Health. Track (2) collaborative discovery of similar genomes between two institutions; and Track (3) data outsourcing to public cloud services. The latter two tracks represent continuing themes from our 2015 competition, while the former was new and a response to a recently established vulnerability. The winning strategy for Track 1 mitigated the privacy risk by hiding approximately 11% of the variation in the database while permitting around 160,000 queries, a significant improvement over the baseline. The winning strategies in Tracks 2 and 3 showed significant progress over the previous competition by achieving multiple orders of magnitude performance improvement in terms of computational runtime and memory requirements. The outcomes suggest that applying highly optimized privacy-preserving and secure computation techniques to safeguard genomic data sharing and analysis is useful. However, the results also indicate that further efforts are needed to refine these techniques into practical solutions.

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Section: Discussionmentioning

confidence: 99%

A community effort to protect genomic data sharing, collaboration and outsourcing

et al. 2017

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“…The first was suggested by Canim et al [16]. Interestingly, the secure component can be part of an untrusted server, for instance in a public cloud [17]. Actually, its characteristics of isolating critical computations on sensitive data from privileged software, for instance the OS, makes it a special case of trusted party based analyses.…”

Section: A Security and Privacy-preserving Technologiesmentioning

confidence: 99%

On Distributed Collaboration for Biomedical Analyses

Boujdad

Gaignard

Südholt

et al. 2019

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

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Cooperation of research groups is nowadays common for the development and execution of biomedical analyses. Multiple partners contribute data in this context, data that is often centralized for processing at some cluster-based or supercomputer-based infrastructure. In contrast, real distributed collaboration that involves processing of data from several partners at different sites is rare. However, such distributed analyses are often very interesting, in particular, for scalability, security and privacy reasons. In this article, we motivate the need for real distributed biomedical analyses in the context of several ongoing projects, including the I-CAN project that involves 34 French hospitals and affiliated research groups. We present a set of distributed architectures for such analyses that we have derived from discussions with different medical research groups and a study of related work. These architectures allow for scalability, security/privacy and reproducibility issues to be taken into account. Finally, we illustrate that these architectures can serve as the basis of a development method for biomedical distributed analyses.

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“…Privacy-preserving query and analysis over genetic data have received much attention recently. Existing works [7,11,16,33,34,37] deal with genome variant query, pattern matching, range query, count query and statistic information computation over genomic data in a privacy-preserving manner. Secure similar sequence query on genomic data, which is the focus of our research, is a special case of secure query processing on genomic data.…”

Section: Related Workmentioning

confidence: 99%

Secure Similar Sequence Query on Outsourced Genomic Data

Cheng¹,

Hou

Wang

2018

Proceedings of the 2018 on Asia Conference on Computer and Communications Security

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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.

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PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension

Cited by 40 publications

References 43 publications

A community effort to protect genomic data sharing, collaboration and outsourcing

A community effort to protect genomic data sharing, collaboration and outsourcing

On Distributed Collaboration for Biomedical Analyses

Secure Similar Sequence Query on Outsourced Genomic Data

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