Secure Testing for Genetic Diseases on Encrypted Genomes with Homomorphic Encryption Scheme

Background Medical data sharing is a big challenge in biomedicine, which often hinders collaborative research. Due to privacy concerns, clinical notes cannot be directly shared. A lot of efforts have been dedicated to de-identifying clinical notes but it is still very challenging to accurately locate and scrub all sensitive elements from notes in an automatic manner. An alternative approach is to remove sentences that might contain sensitive terms related to personal information. Methods A previous study introduced a frequency-based filtering approach that removes sentences containing low frequency bigrams to improve the privacy protection without significantly decreasing the utility. Our work extends this method to consider clinical notes from distributed sources with security and privacy considerations. We developed a novel secure protocol based on private set intersection and secure thresholding to identify uncommon and low-frequency terms, which can be used to guide sentence filtering. Results As the computational cost of our proposed framework mostly depends on the cardinality of the intersection of the sets and the number of data owners, we evaluated the framework in terms of these two factors. Experimental results demonstrate that our proposed method is scalable in various experimental settings. In addition, we evaluated our framework in terms of data utility. This evaluation shows that the proposed method is able to retain enough information for data analysis. Conclusion This work demonstrates the feasibility of using homomorphic encryption to develop a secure and efficient multi-party protocol.

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“…There is a recent application of homomorphic encryption, which can securely perform genome search on a semi-honest cloud server [33].…”

Section: Methodsmentioning

confidence: 99%

A privacy-preserving distributed filtering framework for NLP artifacts

Sadat

Aziz

Mohammed

et al. 2019

BMC Med Inform Decis Mak

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“…Some optimisations have been done, but in the general case, it is still insufficient. HE has been used in literature for private computation on genomic data [63], secure testing for genetic diseases on encrypted genomes [64], and achieve genome-wide association study in [65], which securely and privately examines genetic variations and single-nucleotide polymorphisms of genetic data. b) Searchable Encryption (SE): Classical encryption of data may resolve growing concerns about the protection of outsourced data.…”

Section: Techniques To Support Visionsmentioning

confidence: 99%

Collaborative analysis of genomic data: vision and challenges

Jafarbeiki¹,

Gaire²,

Sakzad³

et al. 2022

Preprint

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“…Tan Ping Zhou et al [16] proposed a secure searching framework of genetic data which are stored in VCF format and are composed essentially of chrome (chr), position (pos), locus (loc), reference (ref), alternate (alt) and type. The proposed optimized model is based on RLWE and Ring-GSW homomorphic encryption algorithms and gives a solution to the KSC17 model [17] with a correction of three errors namely hash collision error (HCE), coefficient combination error (CCE) and losing of partial coefficient error (LPCE).…”

Section: Related Workmentioning

confidence: 99%

Towards Protect a Specific Information in Genomic Data

Achour¹,

Belmadani²

2021

IJIES

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Research on the privacy of human genomic data has significantly increased these last years to find out effective solutions to protect the most critical information resulting from an alignment operation such as the genomic position, cigar and Read content. In this paper, the authors propose an encoding technique based on homomorphic encryption to protect genomic positions of DNA sequences for a secure storage of BAM file. This technique uses two Discrete Gaussian Sampling algorithms for comparison purposes which is Gauss and Knuth-Yao algorithm. The framework was implemented on Java from scratch using container data structure with standard java HashMap for the representation of duplicates positions elements exiting in BAM file. The proposed method was assessed on the NA12878 dataset with initially 2 million positions to encrypt. Obtained results show that the FV-Knuth-Yao technique with HashMap architecture storing 165667 positions and a polynomial degree equal to 64 ensures a fast encryption time estimated to 6,5 minutes. The decryption operation needs 1 hour with zero loss of information. The acceptable security level reached 2 64 operations to break the cryptosystem. To demonstrate the efficiency, security and reliability of our approach, the FV-Knuth-Yao was compared with the OPE Method as implemented in SECRAM tool which is based on a deterministic encryption scheme. Our work mainly focuses on the security of genomic data without compression of BAM file.

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Secure Testing for Genetic Diseases on Encrypted Genomes with Homomorphic Encryption Scheme

Cited by 7 publications

References 21 publications

A privacy-preserving distributed filtering framework for NLP artifacts

A privacy-preserving distributed filtering framework for NLP artifacts

Collaborative analysis of genomic data: vision and challenges

Towards Protect a Specific Information in Genomic Data

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