Private queries on encrypted genomic data

Çetin, Gizem S.; Chen, Hao; Laine, Kim; Lauter, Kristin; Rindal, Peter; Xia, Yuhou

doi:10.1186/s12920-017-0276-z

Cited by 33 publications

(21 citation statements)

References 24 publications

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“…Finally, a secure aggregation over these block-wise Edit distances was applied to approximate the global Edit distance between the sequences. (3) The winning solution of Track 3 from the Microsoft team 42 utilized a technique called permutation-based cuckoo hashing. This method improves the string-matching performance by shortening the strings that need to be homomorphically compared.…”

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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“…278 Hence, an alternative is to utilize encryption and then use searchable encryption to extract the information of interest. Various researchers 251,[278][279][280][281][282][283] online, e-health services, patient monitoring, wireless applications, and nursing call centers. 284 In EHR systems, the details of a patient are entered, which are utilized by the doctors for diagnosis of the disease.…”

Section: Encrypted Genomic Datamentioning

confidence: 99%

Searchable encryption: A survey on privacy‐preserving search schemes on encrypted outsourced data

Handa

Krishna

Aggarwal

2019

Concurrency and Computation

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Outsourcing confidential data to cloud storage leads to privacy challenges that can be reduced using encryption. However, with encryption in place, the utilization of the data is reduced, which leads to reduced quality of experience of the users. To overcome this, searchable encryption (SE) schemes are utilized, which allow the end users to retrieve the relevant documents from the cloud, for which various researchers have worked utilizing different techniques. Despite the popularity of the searchable encryption schemes, most of the surveys either do not provide or present an incomplete taxonomy of SE schemes. Hence, in this paper, we attempt to present a complete taxonomy/classification of the searchable encryption schemes in terms of the type of search, type of index, results retrieved, implementation type, multiplicity of users, and the technique used. From the literature, it is observed that inner product similarity is widely adopted by researchers to compute the similarity of the query and the document index as it provides both conjunctive and disjunctive searching (ie, have better search capability) but requires high search time (ie, have lower search efficiency). On the other hand, schemes based on binary comparisons exist, which require less search time (ie, have better search efficiency) but support only conjunctive searching (ie, have limited search capability). Thus, a major conclusion drawn from our work is that there is an imbalance between search capability and search efficiency, ie, in the existing schemes, search capability can be improved at the cost of search time only. Therefore, we suggest that one direction where researchers should work on is to provide a balance between search capability and search efficiency.

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“…The specific application addressed focuses on filtering information based on homomorphic techniques and the objective is to retrieve medical information without mismatch or other accidental acquisition of sensitive informa-tion [51,19,34,66]. The studies that focused on the application of homomorphic encryption in health care systems include Tang et al [67], Çetin et al [68], Emmanuel et al [22], [23], Bocu and Costache [69], and Wang and Zhang [70]. Some other applications include encryption of biomarkers and other genetic data, especially given that the medical fraternity is moving towards personalized health [71,72,18,73,74].…”

Section: Big Data and Cloud Computingmentioning

confidence: 99%

A systematic review on the status and progress of homomorphic encryption technologies

Alloghani

Alani

Al‐Jumeily

et al. 2019

Journal of Information Security and Applications

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With the emergence of big data and the continued growth in cloud computing applications, serious security and privacy concerns emerged. Consequently, several researchers and cybersecurity experts have embarked on a quest to extend data encryption to big data systems and cloud computing applications. As most cloud users turn to using public cloud services, confidentiality becomes and even more complicated issue. Cloud clients storing their data on a public cloud always seek solutions to confidentiality problem. Homomorphic encryption emerged as a possible solution where client's data is encrypted on the cloud in a way that allows some search and manipulation operations without proper decryption. In this paper, we present a systematic review of research paper published in the field of homomorphic encryption. This paper uses PRISMA checklist alongside some items of Cochrane's Quality Assessment to review studies retrieved from various resources. It was highly noticeable in the reviewed papers that security in big data and cloud computing has received most attention. Most papers suggested the use of homomorphic encryption although the thematic analysis has identified other potential concerns. Regarding the quality of the articles, 38% of the articles failed to meet three checklist items, including explicit statement of research objectives, procedure recognition and sources of funding used in the study. The review also presented compendium textual analysis of di erent homomorphic encryption algorithms, application areas, and areas of future developments. Results of the evaluation through PRISMA and the Cochrane tool showed that a majority of research articles discussed the potential use and application of Homomorphic Encryption as a solution to the growing demands of big data and absence of security and privacy mechanisms therein. This was evident from 26 of the total 59 articles that met the inclusion criteria. The term Homomorphic Encryption appeared 1802 times in the word cloud derived from the selected articles, which speaks of its potential to ensure security and privacy, while also preserving the CIA triad in the context of big data and cloud computing.

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Private queries on encrypted genomic data

Cited by 33 publications

References 24 publications

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

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

Searchable encryption: A survey on privacy‐preserving search schemes on encrypted outsourced data

A systematic review on the status and progress of homomorphic encryption technologies

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