Privacy Of Genomic Data Research Articles

Genomic Data Privacy and the Right to Science: Issues of Balance

The paper examines the confidentiality of genomic data. According to the author, genomic data is a special concept in the field of personal data and requires increased regulatory guarantees of legal protection and safeguards. In addition, the author considers the issue of the grounds for disclosure and use of this type of information. According to the researcher, this is possible only if there is a public interest in conducting scientific research that contributes to the implementation of the health function by the state. The right to science, enshrined in the Universal Declaration of Human Rights of 1948, can serve as a regulatory basis for this. The author notes that this right has not yet been established at the level of national legislation, which significantly complicates its understanding and definition of restrictions in its implementation. The paper also emphasizes the uniqueness of genomic information, which is characterized by features of identifiability and relative danger. These features are specified by the author in terms of the purpose and scope of the use of genetic data, their informational content, as well as the degree of their influence on the rights of the relevant person. In conclusion, the author puts forward am idea of one best way to balance the right to science and genomic data privacy, through which it will be possible to ensure both effective legal protection and a stable course of scientific evolution and progress.

Assuring Data Integrity, Preservation, Transparency and Privacy of Genomic Data in the Sharing Process Using Blockchain Technology

Assuring Data Integrity, Preservation, Transparency and Privacy of Genomic Data in the Sharing Process Using Blockchain Technology

Privacy-Preserving Fingerprinting Against Collusion and Correlation Threats in Genomic Data

Sharing genomic databases is critical to the collaborative research in computational biology. A shared database is more informative than specific genome-wide association studies (GWAS) statistics as it enables do-it-yourself calculations. Genomic databases involve intellectual efforts from the curator and sensitive information of participants, thus in the course of data sharing, the curator (database owner) should be able to prevent unauthorized redistributions and protect genomic data privacy. As it becomes increasingly common for a single database be shared with multiple recipients, the shared genomic database should also be robust against collusion attack, where multiple malicious recipients combine their individual copies to forge a pirated one with the hope that none of them can be traced back. The strong correlation among genomic entries also make the shared database vulnerable to attacks that leverage the public correlation models. In this paper, we assess the robustness of shared genomic database under both collusion and correlation threats. To this end, we first develop a novel genomic database fingerprinting scheme, called Gen-Scope. It achieves both copyright protection (by enabling traceability) and privacy preservation (via local differential privacy) for the shared genomic databases. To defend against collusion attacks, we augment Gen-Scope with a powerful traitor tracing technique, i.e., the Tardos codes. Via experiments using a real-world genomic database, we show that Gen-Scope achieves strong fingerprint robustness, e.g., the fingerprint cannot be compromised even if the attacker changes 45% of the entries in its received fingerprinted copy and colluders will be detected with high probability. Additionally, Gen-Scope outperforms the considered baseline methods. Under the same privacy and copyright guarantees, the accuracy of the fingerprinted genomic database obtained by Gen-Scope is around 10% higher than that achieved by the baseline, and in terms of preservations of GWAS statistics, the consistency of variant-phenotype associations can be about 20% higher. Notably, we also empirically show that Gen-Scope can identify at least one of the colluders even if malicious receipts collude after independent correlation attacks.

An Analysis of Ethics-Based Foundation and Regulatory Issues for Genomic Data Privacy

An Analysis of Ethics-Based Foundation and Regulatory Issues for Genomic Data Privacy

Finding Highly Similar Regions of Genomic Sequences Through Homomorphic Encryption.

Finding highly similar regions of genomic sequences is a basic computation of genomic analysis. Genomic analyses on a large amount of data are efficiently processed in cloud environments, but outsourcing them to a cloud raises concerns over the privacy and security issues. Homomorphic encryption (HE) is a powerful cryptographic primitive that preserves privacy of genomic data in various analyses processed in an untrusted cloud environment. We introduce an efficient algorithm for finding highly similar regions of two homomorphically encrypted sequences, and describe how to implement it using the bit-wise and word-wise HE schemes. In the experiment, our algorithm outperforms an existing algorithm by up to two orders of magnitude in terms of elapsed time. Overall, it finds highly similar regions of the sequences in real data sets in a feasible time.

Maliciously Secure and Efficient Large-Scale Genome-Wide Association Study With Multi-Party Computation

Genome-Wide Association Study (GWAS) aims at detecting the association between diseases and Single-Nucleotide Polymorphisms (SNPs) with statistical techniques and has great potential for disease diagnosis. To obtain high-quality results, GWAS requires large-scale genomic data containing individuals’ privacy information. Thus, how to improve the efficiency of GWAS while protecting the privacy of genomic data becomes a critical challenge. In this paper, we propose a secure and efficient GWAS scheme. By using secure three-party computation, we present a series of protocols, i.e., Secure Quality Control, Secure Principle Component Analysis, Secure Cochran-Armitage trend test, and Secure Logistic Regression, to cover the most significant procedures of secure GWAS. In these protocols, a new comparison protocol is designed to reduce communication and improve efficiency. Furthermore, by extending the above comparison protocol to be maliciously secure and utilizing other technologies, e.g., consistency check, we extend the whole GWAS scheme to malicious security with rationally additional overhead. Experimental results demonstrate that our protocols achieve about 33% performance improvement than the state-of-art secure GWAS scheme using two-party computation in terms of runtime and communication in the semi-honest setting. The cost of our scheme in the malicious setting is around 1.5X than that in the semi-honest setting.

“Not Private at All:” Comparative Perspectives on Privacy of Genomic Data, Family History Data, Health-Related Data, and Other Personal Data

People choose how and if to generate and disclose not just personal genomic data, but also multiple other types of personal health and non-health related data. To contextualize choices about genetic testing and genetic data disclosure, we explored perspectives of genomic data privacy and disclosure compared to other types of data. We conducted a qualitative focus group study with adult members of an integrated U.S. health system, using administrative data to stratify our sample by age and by race/ethnicity. Discussion topics included qualities, rights, benefits and harms of disclosure of genomic, health, family history and non-health related data. We conducted thematic template analysis using verbatim transcripts. The sample (n = 24) was 67% female, mean age 54.1 years (range 23-88), and 37% people of color; 71% reported college degree. Participants considered genetic data, but not other data types, as a permanent, core part of the individual self and as protected health information under current laws. Participants did not feel that individuals had a right to family medical history disclosure from their relatives. Participants assumed high levels of privacy protections of genetic and other health-related data, but no perceived privacy or protection around other personal data. Participants weighed benefits and risks of generation and disclosure of all data types; harms were more far-reaching for non-health data, possibly related to the perceived lack of protections around these data. People make privacy-related choices about genetic testing in the context of related considerations for multiple types of data and rely on perceived privacy protections under current U.S. health privacy laws. Genetic research and screening programs should consider providing clear guidance on privacy protections afforded to genetic information in U.S. clinical settings. Future research should examine connections between privacy-related views on genetic and multiple other types of personal data.

Generalized genomic data sharing for differentially private federated learning

The success behind Machine Learning (ML) methods has largely been attributed to the quality and quantity of the available data which can spread across multiple owners. A Federated Learning (FL) from distributed datasets often provides a reliable solution that provides valuable insight. For a genomic dataset, such data have also proven to be sensitive which requires additional safety mechanisms before any sharing or ML operations. We propose a generalized gene expression data sharing method using a differentially private mechanism. Due to the large number of genes available, the data dimension is also reduced to accommodate smaller privacy budgets as we utilize an exponential mechanism to create a private histogram from numeric expression data. The output histogram can be used in any federated machine learning setting having multiple data owners. The proposed solution was submitted to genomic data security and privacy competition, iDash 2020 where it ranked third among 55 teams. We extend the proposed solution and experimented with two different machine learning algorithms and different settings. The experimental results show that it takes around 8 s to train a model while achieving 0.89 AUC with only a privacy budget of 5. The paper outlined a method to share gene expression data for Federated Learning using a privacy-preserving mechanism. Different experimental settings and recent competition results show the efficacy of the method which can be further extended to other genomic datasets and machine learning algorithms.

CURRENT EVIDENCE ON BIOINFORMATICS ROLE AND DIGITAL FORENSICS THAT CONTRIBUTE TO FORENSIC SCIENCE: UPCOMING THREAT

Forensics has become an essential part of the disclosure of criminal evidence. A bioinformatics approach in the form of DNA forensics and digital forensics can be a good combination in disclosing digital-based criminal evidence. This study explains how the role of bioinformatics through the digital approach can be a means of forming new approaches in integration with digital forensics, called cyber-bioinformatics. Despite many hopes and challenges ahead, it does not rule out the possibility of criminal cases related to the privacy of human genomic data, so it proposes a new hypothesis, “cyber-bioinformatics.” The role of cyber-bioinformatics is very central in this regard.

Sociotechnical safeguards for genomic data privacy.

Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of genomic data that are collected, used and shared. This state of affairs raises new and challenging concerns for personal privacy, both legally and technically. This Review appraises existing and emerging threats to genomic data privacy and discusses how well current legal frameworks and technical safeguards mitigate these concerns. It concludes with a discussion of remaining and emerging challenges and illustrates possible solutions that can balance protecting privacy and realizing the benefits that result from the sharing of genetic information.

OP043: The evolving attitude towards privacy and security of personal genomic data

Usage of genomic testing, including multi-gene panel, genomic sequencing (GS) and Direct-to-Consumer testing, continues to grow in both medical settings and for personal use. Much has been written about the concerns prospective users of genomic testing have had about the privacy and security of their genomic test results. Predominate concerns reported include that genomic information could be used by insurance companies to deny insurance or raise premiums beyond their ability to afford it, employment discrimination and the use of genomic data without consent.

Who\u2019s afraid of genetic tests?: An assessment of Singapore\u2019s public attitudes and changes in attitudes after taking a genetic test

BackgroundAs a consequence of precision medicine initiatives, genomic technologies have rapidly spread around the world, raising questions about genetic privacy and the ethics of data sharing. Previous scholarship in bioethics and science and technology studies has made clear that different nations have varying expectations about trust, transparency, and public reason in relation to emerging technologies and their governance. The key aims of this article are to assess genetic literacy, perceptions of genetic testing, privacy concerns, and governing norms amongst the Singapore population by collecting surveys.MethodsThis study investigated genetic literacy and broad public attitudes toward genetic tests in Singapore with an online public survey (n = 560). To assess potential changes in attitudes following receipt of results from a genetic test, we also surveyed undergraduate students who underwent a genetic screen as part of a university class before and after they received their test results (n = 25).ResultsPublic participants showed broad support for the use of genetic tests; scored an average of 48.9% in genetic literacy; and expressed privacy concerns over data sharing and a desire for control over their genetic data. After taking a genetic test and receiving genetic test results, students reported less fear of genetic tests while other attitudes did not change significantly.ConclusionThese findings highlight the potential of genetic education and active engagement with genetic testing to increase support and participation in genomic projects, PM, and biobanking initiatives; and they suggest that data privacy protections could potentially reduce discrimination by giving participants control over who can access their data. More specifically, these findings and the dataset we provide may be helpful in formulating culturally sensitive education programs and regulations concerning genomic technologies and data privacy.

Blockchain Applications in Health Care and Public Health: Increased Transparency.

BackgroundAlthough big data and smart technologies allow for the development of precision medicine and predictive models in health care, there are still several challenges that need to be addressed before the full potential of these data can be realized (eg, data sharing and interoperability issues, lack of massive genomic data sets, data ownership, and security and privacy of health data). Health companies are exploring the use of blockchain, a tamperproof and distributed digital ledger, to address some of these challenges.ObjectiveIn this viewpoint, we aim to obtain an overview of blockchain solutions that aim to solve challenges in health care from an industry perspective, focusing on solutions developed by health and technology companies.MethodsWe conducted a literature review following the protocol defined by Levac et al to analyze the findings in a systematic manner. In addition to traditional databases such as IEEE and PubMed, we included search and news outlets such as CoinDesk, CoinTelegraph, and Medium.ResultsHealth care companies are using blockchain to improve challenges in five key areas. For electronic health records, blockchain can help to mitigate interoperability and data sharing in the industry by creating an overarching mechanism to link disparate personal records and can stimulate data sharing by connecting owners and buyers directly. For the drug (and food) supply chain, blockchain can provide an auditable log of a product’s provenance and transportation (including information on the conditions in which the product was transported), increasing transparency and eliminating counterfeit products in the supply chain. For health insurance, blockchain can facilitate the claims management process and help users to calculate medical and pharmaceutical benefits. For genomics, by connecting data buyers and owners directly, blockchain can offer a secure and auditable way of sharing genomic data, increasing their availability. For consent management, as all participants in a blockchain network view an immutable version of the truth, blockchain can provide an immutable and timestamped log of consent, increasing transparency in the consent management process.ConclusionsBlockchain technology can improve several challenges faced by the health care industry. However, companies must evaluate how the features of blockchain can affect their systems (eg, the append-only nature of blockchain limits the deletion of data stored in the network, and distributed systems, although more secure, are less efficient). Although these trade-offs need to be considered when viewing blockchain solutions, the technology has the potential to optimize processes, minimize inefficiencies, and increase trust in all contexts covered in this viewpoint.

P2GT: Fine-Grained Genomic Data Access Control With Privacy-Preserving Testing in Cloud Computing.

With the rapid development of bioinformatics and the availability of genetic sequencing technologies, genomic data has been used to facilitate personalized medicine. Cloud computing, features as low cost, rich storage and rapid processing can precisely respond to the challenges brought by the emergence of massive genomic data. Considering the security of cloud platform and the privacy of genomic data, we first introduce P2GT which utilizes key-policy attribute-based encryption to realize genomic data access control with unbounded attributes, and employs equality test algorithm to achieve personalized medicine test by matching digitized single nucleotide polymorphisms (SNPs) directly on the users' ciphertext without encrypting multiple times. We then propose an enhanced scheme P2GT+, which adopts identity-based encryption with equality test supporting flexible joint authorization to realize privacy-preserving paternity test, genetic compatibility test and disease susceptibility test over the encrypted SNPs with P2GT. We prove the security of proposed schemes and conduct extensive experiments with the 1,000 Genomes dataset. The results show that P2GT and P2GT+ are practical and scalable enough to meet the privacy-preserving and authorized genetic testing requirements in cloud computing.

SM algorithms-based encryption scheme for large genomic data files

With the rapid development of the genomic sequencing technology, the cost of obtaining personal genomic data and effectively analyzing it has been gradually reduced. The analysis and utilization of genomic data gradually entered the public view, and the leakage of genomic data privacy has attracted the attention of researchers. The security of genomic data is not only related to the protection of personal privacy, but also related to the biological information security of the country. However, there is still no effective genomic data privacy protection scheme using Shangyong Mima(SM) algorithms. In this paper, we analyze the widely used genomic data file formats and design a large genomic data files encryption scheme based on the SM algorithms. Firstly, we design a key agreement protocol based on the SM2 asymmetric cryptography and use the SM3 hash function to guarantee the correctness of the key. Secondly, we used the SM4 symmetric cryptography to encrypt the genomic data by optimizing the packet processing of files, and improve the usability by assisting the computing platform with key management. Software implementation demonstrates that the scheme can be applied to securely transmit the genomic data in the network environment and provide an encryption method based on SM algorithms for protecting the privacy of genomic data.

A secure system for genomics clinical decision support.

We developed a prototype genomic archiving and communications system to securely store genome data and provide clinical decision support (CDS). This system operates on a client-server model. The client encrypts the data, and the server stores data and performs the computations necessary for CDS. Computations are directly performed on encrypted data, and the client decrypts results. The server cannot decrypt inputs or outputs, which provides strong guarantees of security. We have validated our system with three genomics-based CDS applications. The results demonstrate that it is possible to resolve a long-standing dilemma in genomic data privacy and accessibility, by using a principled cryptographical framework and a mathematical representation of genome data and CDS questions.

Ensuring privacy and security of genomic data and functionalities

In recent times, the reduced cost of DNA sequencing has resulted in a plethora of genomic data that is being used to advance biomedical research and improve clinical procedures and healthcare delivery. These advances are revolutionizing areas in genome-wide association studies (GWASs), diagnostic testing, personalized medicine and drug discovery. This, however, comes with security and privacy challenges as the human genome is sensitive in nature and uniquely identifies an individual. In this article, we discuss the genome privacy problem and review relevant privacy attacks, classified into identity tracing, attribute disclosure and completion attacks, which have been used to breach the privacy of an individual. We then classify state-of-the-art genomic privacy-preserving solutions based on their application and computational domains (genomic aggregation, GWASs and statistical analysis, sequence comparison and genetic testing) that have been proposed to mitigate these attacks and compare them in terms of their underlining cryptographic primitives, security goals and complexities-computation and transmission overheads. Finally, we identify and discuss the open issues, research challenges and future directions in the field of genomic privacy. We believe this article will provide researchers with the current trends and insights on the importance and challenges of privacy and security issues in the area of genomics.

Achieving differential privacy of genomic data releasing via belief propagation

Privacy preserving data releasing is an important problem for reconciling data openness with individual privacy. The state-of-the-art approach for privacy preserving data release is differential privacy, which offers powerful privacy guarantee without confining assumptions about the background knowledge about attackers. For genomic data with huge-dimensional attributes, however, current approaches based on differential privacy are not effective to handle. Specifically, amount of noise is required to be injected to genomic data with tens of million of SNPs (Single Nucleotide Polymorphisms), which would significantly degrade the utility of released data. To address this problem, this paper proposes a differential privacy guaranteed genomic data releasing method. Through executing belief propagation on factor graph, our method can factorize the distribution of sensitive genomic data into a set of local distributions. After injecting differential-privacy noise to these local distributions, synthetic sensitive data can be obtained by sampling on noise distribution. Synthetic sensitive data and factor graph can be further used to construct approximate distribution of non-sensitive data. Finally, non-sensitive genomic data is sampled from the approximate distribution to construct a synthetic genomic dataset.

Protecting Privacy and Security of Genomic Data in i2b2 with Homomorphic Encryption and Differential Privacy.

Re-use of patients' health records can provide tremendous benefits for clinical research. Yet, when researchers need to access sensitive/identifying data, such as genomic data, in order to compile cohorts of well-characterized patients for specific studies, privacy and security concerns represent major obstacles that make such a procedure extremely difficult if not impossible. In this paper, we address the challenge of designing and deploying in a real operational setting an efficient privacy-preserving explorer for genetic cohorts. Our solution is built on top of the i2b2 (Informatics for Integrating Biology and the Bedside) framework and leverages cutting-edge privacy-enhancing technologies such as homomorphic encryption and differential privacy. Solutions involving homomorphic encryption are often believed to be costly and immature for use in operational environments. Here, we show that, for specific applications, homomorphic encryption is actually a very efficient enabler. Indeed, our solution outperforms prior work by enabling a researcher to securely compute simple statistics on more than 3,000 encrypted genetic variants simultaneously for a cohort of 5,000 individuals in less than 5 seconds with commodity hardware. To the best of our knowledge, our privacy-preserving solution is the first to also be successfully deployed and tested in a operation setting (Lausanne University Hospital).

Genomic Data Privacy and Security: Where We Stand and Where We Are Heading

Genomic data has the potential to be highly sensitive. Thus, privacy and security are considered paramount when collecting or sharing such data. This special issue explores the risks of genomic data storage. Furthermore, it provides an overview of solutions ensuring the integrity and privacy of genomic computation.