Journal articles: 'Genomic security and privacy'

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Mohammed Yakubu, Abukari, and Yi-Ping Phoebe Chen. "Ensuring privacy and security of genomic data and functionalities." Briefings in Bioinformatics 21, no. 2 (February 12, 2019): 511–26. http://dx.doi.org/10.1093/bib/bbz013.

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Abstract 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.

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Schwab, Abraham P., Hung S. Luu, Jason Wang, and Jason Y. Park. "Genomic Privacy." Clinical Chemistry 64, no. 12 (December 1, 2018): 1696–703. http://dx.doi.org/10.1373/clinchem.2018.289512.

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Abstract BACKGROUND Genetic information is unique among all laboratory data because it not only informs the current health of the specific person tested but may also be predictive of the future health of the individual and, to varying degrees, all biological relatives. CONTENT As DNA sequencing has become ubiquitous with decreasing cost, large repositories of genomic data have emerged from the domains of research, healthcare, law enforcement, international security, and recreational consumer interest (i.e., genealogy). Broadly shared genomic data are believed to be a key element for future discoveries in human disease. For example, the National Cancer Institute's Genomic Data Commons is designed to promote cancer research discoveries by providing free access to the genome data sets of 12000 cancer patients. However, in parallel with the promise of curing diseases, genomic data also have the potential for harm. Genomic data that are deidentified by standard healthcare practices (e.g., removal of name, date of birth) can be reidentified by methods that combine genomic software with publicly available demographic databases (e.g., phone book). Recent law enforcement cases (i.e., Bear Brook Murders, Golden State Killer) in the US have demonstrated the power of combining DNA profiles with genealogy databases. SUMMARY We examine the current environment of genomic privacy and confidentiality in the US and describe current and future risks to genomic privacy. Reidentification and inference of genetic information of biological relatives will become more important as larger databases of clinical, criminal, and recreational genomic information are developed over the next decade.

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Park, Young-Hoon, Yejin Kim, and Junho Shim. "Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy." Electronics 10, no. 23 (December 3, 2021): 3019. http://dx.doi.org/10.3390/electronics10233019.

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The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.

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Llorente, Silvia, and Jaime Delgado. "Implementation of Privacy and Security for a Genomic Information System Based on Standards." Journal of Personalized Medicine 12, no. 6 (May 31, 2022): 915. http://dx.doi.org/10.3390/jpm12060915.

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Genomic information is a very sensitive type of digital information as it not only applies to a person, but also to close relatives. Therefore, privacy provision is key to protecting genomic information from unauthorized access. It is worth noting that most of the current genomic information formats do not provide specific mechanisms by which to secure the stored information. In order to solve, among other things, the privacy provision issue, we proposed the GIPAMS (Genomic Information Protection And Management System) modular architecture, which is based on the use of standards such as ISO/IEC 23092 and a few GA4GH (Global Alliance for Genomics and Health) initiatives. Some of the GIPAMS modules have already been implemented, mainly based on ISO/IEC 23092 features, and we are conducting work on the complete version of the architecture, and other standards are also considered. One of the objectives of GIPAMS is to enable the management of different formats of genomic information in a unique and interoperable way, providing privacy and security for formats that do not currently support them.

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Mittos, Alexandros, Bradley Malin, and Emiliano De Cristofaro. "Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective." Proceedings on Privacy Enhancing Technologies 2019, no. 1 (January 1, 2019): 87–107. http://dx.doi.org/10.2478/popets-2019-0006.

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Abstract Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy experts.

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Asgiani, Piping, Chriswardani Suryawati, and Farid Agushybana. "A literature review: Security Aspects in the Implementation of Electronic Medical Records in Hospitals." MEDIA ILMU KESEHATAN 10, no. 2 (January 29, 2022): 161–66. http://dx.doi.org/10.30989/mik.v10i2.561.

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Backgrounds: Electronic Medical Records have complete and integrated patient health data, and are up to date because RME combines clinical and genomic data, this poses a great risk to data disclosure The priority of privacy is data security (security) so that data will not leak to other parties. That way cyber attacks can be suppressed by increasing cybersecurity, namely conducting regular evaluation and testing of security levels.Objectives: To determine the security technique that maintains privacy of electronic medical records.Methods: This type of research uses a literature review methodResults: Data security techniques are determined from each type of health service. Data security techniques that can be applied are cryptographic methods, firewalls, access control, and other security techniques. This method has proven to be a very promising and successful technique for safeguarding the privacy and security of RMEConclusion: Patient medical records or medical records are very private and sensitive because they store all data about complaints, diagnoses, disease histories, actions, and treatments about patients, so the information contained therein must be kept confidential. As well as the hospital as a medical record manager is required to apply for patient privacy data security techniques.

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Villanueva, Angela G., Robert Cook-Deegan, Jill O. Robinson, Amy L. McGuire, and Mary A. Majumder. "Genomic Data-Sharing Practices." Journal of Law, Medicine & Ethics 47, no. 1 (2019): 31–40. http://dx.doi.org/10.1177/1073110519840482.

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Making data broadly accessible is essential to creating a medical information commons (MIC). Transparency about data-sharing practices can cultivate trust among prospective and existing MIC participants. We present an analysis of 34 initiatives sharing DNA-derived data based on public information. We describe data-sharing practices captured, including practices related to consent, privacy and security, data access, oversight, and participant engagement. Our results reveal that data-sharing initiatives have some distance to go in achieving transparency.

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Lerner, Barbara, Deborah Passey, Nina Sperber, and Sara Knight. "OP043: The evolving attitude towards privacy and security of personal genomic data." Genetics in Medicine 24, no. 3 (March 2022): S369. http://dx.doi.org/10.1016/j.gim.2022.01.590.

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Carter, Alexis B. "Considerations for Genomic Data Privacy and Security when Working in the Cloud." Journal of Molecular Diagnostics 21, no. 4 (July 2019): 542–52. http://dx.doi.org/10.1016/j.jmoldx.2018.07.009.

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Doll, Bruce, Mauricio J. De Castro, Melissa H. Fries, Arnyce R. Pock, Diane Seibert, and Wendy Yang. "Precision Medicine—A Demand Signal for Genomics Education." Military Medicine 187, Supplement_1 (December 30, 2021): 40–46. http://dx.doi.org/10.1093/milmed/usab406.

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ABSTRACT Pressed by the accumulating knowledge in genomics and the proven success of the translation of cancer genomics to clinical practice in oncology, the Obama administration unveiled a $215 million commitment for the Precision Medicine Initiative (PMI) in 2016, a pioneering research effort to improve health and treat disease using a new model of patient-powered research. The objectives of the initiative include more effective treatments for cancer and other diseases, creation of a voluntary national research cohort, adherence to privacy protections for maintaining data sharing and use, modernization of the regulatory framework, and forging public–private partnerships to facilitate these objectives. Specifically, the DoD Military Health System joined other agencies to execute a comprehensive effort for PMI. Of the many challenges to consider that may contribute to the implementation of genomics—lack of familiarity and understanding, poor access to genomic medicine expertise, needs for extensive informatics and infrastructure to integrate genomic results, privacy and security, and policy development to address the unique requirements of military medical practice—we will focus on the need to establish education in genomics appropriate to the provider’s responsibilities. Our hypothesis is that there is a growing urgency for the development of educational experiences, formal and informal, to enable clinicians to acquire competency in genomics commensurate with their level of practice. Several educational approaches, both in practice and in development, are presented to inform decision-makers and empower military providers to pursue courses of action that respond to this need.

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Rogith, Deevakar, Rafeek Adeyemi Yusuf, Shelly Renee Anstey Hovick, Susan K. Peterson, Allison Michelle Burton-Chase, Yisheng Li, Funda Meric-Bernstam, and Elmer Victor Bernstam. "Attitudes regarding privacy of genomic information in personalized cancer therapy." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 9576. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.9576.

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9576 Background: Cancer therapy is increasingly personalized to the molecular characteristics of a particular patient and his/her tumor. Patients and providers express interest in personalized therapy yet concerns regarding the privacy of genomic data have been raised, particularly in the context of research. We evaluated patients’ attitudes regarding privacy of genomic data. Methods: Newly registered female breast cancer patients at MD Anderson Cancer Center were invited to participate. Of 308 consecutive patients approached, 100 completed a survey assessing attitudes regarding association of personal identifying information with genomic data, risks for potential insurance and employment discrimination based on genomic information, and willingness to share genomic data (32% response rate). Results: Most patients (83%) indicated that genomic data should be protected. However, only 13% endorsed concern regarding genomic data privacy, measured using a composite scale (α= 0.92). Patients expressed more concern about insurance discrimination than employment discrimination (43% vs. 28%, p<0.001), and these two variables were highly correlated (χ2=32.7, p<0.001). Patients expressed greater trust in research institutions like MD Anderson to protect the security of their molecular data compared with government agencies or drug companies (80% vs. 63% vs. 56%; p < 0.001). Most did not endorse concern regarding association of their genomic data with their name and identities (51%), billing and insurance (56%), or clinical data (73%). Patients were more willing to share de-identified data than identified data with researchers other than their treating physicians (p<0.001). 36% of patients were willing to share identified data with any MD Anderson researcher and 14% with any cancer researcher. Conclusions: Patients generally expressed low levels of concern regarding privacy of genomic data. Cancer patients may recognize the clinical and research value of genomic testing and a significant proportion are willing to share their genomic data with researchers.

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Hubaux, Jean-Pierre, Stefan Katzenbeisser, and Bradley Malin. "Genomic Data Privacy and Security: Where We Stand and Where We Are Heading." IEEE Security & Privacy 15, no. 5 (2017): 10–12. http://dx.doi.org/10.1109/msp.2017.3681048.

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Pascoal, Túlio, Jérémie Decouchant, Antoine Boutet, and Marcus Völp. "I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies." Proceedings on Privacy Enhancing Technologies 2023, no. 1 (January 2023): 437–54. http://dx.doi.org/10.56553/popets-2023-0026.

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Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, careless sharing of GWAS statistics might give rise to privacy attacks. Several works attempted to reconcile secure processing with privacy-preserving releases of GWASes. However, we highlight that these approaches remain vulnerable if GWASes utilize overlapping sets of individuals and genomic variations. In such conditions, we show that even when relying on state-of-the-art techniques for protecting releases, an adversary could reconstruct the genomic variations of up to 28.6% of participants, and that the released statistics of up to 92.3% of the genomic variations would enable membership inference attacks. We introduce I-GWAS, a novel framework that securely computes and releases the results of multiple possibly interdependent GWASes. I-GWAS continuously releases privacy-preserving and noise-free GWAS results as new genomes become available.

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Bahmani, Amir, Kyle Ferriter, Vandhana Krishnan, Arash Alavi, Amir Alavi, Philip S. Tsao, Michael P. Snyder, and Cuiping Pan. "Swarm: A federated cloud framework for large-scale variant analysis." PLOS Computational Biology 17, no. 5 (May 12, 2021): e1008977. http://dx.doi.org/10.1371/journal.pcbi.1008977.

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Genomic data analysis across multiple cloud platforms is an ongoing challenge, especially when large amounts of data are involved. Here, we present Swarm, a framework for federated computation that promotes minimal data motion and facilitates crosstalk between genomic datasets stored on various cloud platforms. We demonstrate its utility via common inquiries of genomic variants across BigQuery in the Google Cloud Platform (GCP), Athena in the Amazon Web Services (AWS), Apache Presto and MySQL. Compared to single-cloud platforms, the Swarm framework significantly reduced computational costs, run-time delays and risks of security breach and privacy violation.

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Wong, David C. S., Maximiliano Olivera, Jing Yu, Anita Szabo, Ismail Moghul, Konstantinos Balaskas, Robert Luben, Anthony P. Khawaja, Nikolas Pontikos, and Pearse A. Keane. "Cloud-based genomics pipelines for ophthalmology: reviewed from research to clinical practice." Modeling and Artificial Intelligence in Ophthalmology 3, no. 1 (September 17, 2021): 101–40. http://dx.doi.org/10.35119/maio.v3i1.115.

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Aim: To familiarize clinicians with clinical genomics, and to describe the potential of cloud computing for enabling the future routine use of genomics in eye hospital settings.Design: Review article exploring the potential for cloud-based genomic pipelines in eye hospitals.Methods: Narrative review of the literature relevant to clinical genomics and cloud computing, using PubMed and Google Scholar. A broad overview of these fields is provided, followed by key examples of their integration.Results: Cloud computing could benefit clinical genomics due to scalability of resources, potentially lower costs, and ease of data sharing between multiple institutions. Challenges include complex pricing of services, costs from mistakes or experimentation, data security, and privacy concerns.Conclusions and future perspectives: Clinical genomics is likely to become more routinely used in clinical practice. Currently this is delivered in highly specialist centers. In the future, cloud computing could enable delivery of clinical genomics services in non-specialist hospital settings, in a fast, cost-effective way, whilst enhancing collaboration between clinical and research teams.

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Бородин, Сергей, and Андрей Инюшкин. "GENOMIC RESEARCHES AND GENOMIC EDITING IN TERMS OF FORMING EFFECTIVE LEGAL REGIME." Rule-of-law state: theory and practice 16, no. 2 (February 1, 2020): 19–31. http://dx.doi.org/10.33184/pravgos-2020.2.3.

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He use of modern technology has created new opportunities for genetic researches and product creation through genome editing. At the same time, the use of these technologies without ensuring the foundations of the rule of law, including moral issues, creates a significant risk of infringement of public and private interests. Aim: to analyze the regulation of transferring and processing of personal data, including those related to genetic information, and the creation of new results of intellectual activity and products created by genome editing. Methods: methods of formal and dialectical logic, description methods, comparative legal methods. Results: the study allows us to identify the main currently used options for protecting privacy secrets during genetic researches and formulate a proposal on securing at the international level the minimum requirements for data placement, including the use of virtual computers and data management in the cloud. It is proposed to use databases as the main object of intellectual property for the formation of the legal regime. It is concluded that judicial practice is formed in the direction of considering researches and certain manipulations with genetic material precisely from the position of a balance of public interests (morality) and private non-property interests (respect for private and family life), regardless of the assessment of any property interests in relation to the relevant objects.

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Townend, David. "EU Laws on Privacy in Genomic Databases and Biobanking." Journal of Law, Medicine & Ethics 44, no. 1 (2016): 128–42. http://dx.doi.org/10.1177/1073110516644204.

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Both the European Union and the Council of Europe have a bearing on privacy in genomic databases and biobanking. In terms of legislation, the processing of personal data as it relates to the right to privacy is currently largely regulated in Europe by Directive 95/46/EC, which requires that processing be “fair and lawful” and follow a set of principles, meaning that the data be processed only for stated purposes, be sufficient for the purposes of the processing, be kept only for so long as is necessary to achieve those purposes, and be kept securely and only in an identifiable state for such time as is necessary for the processing. The European privacy regime does not require the de-identification (anonymization) of personal data used in genomic databases or biobanks, and alongside this practice informed consent as well as governance and oversight mechanisms provide for the protection of genomic data.

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McGuire, Amy L., Rebecca Fisher, Paul Cusenza, Kathy Hudson, Mark A. Rothstein, Deven McGraw, Stephen Matteson, John Glaser, and Douglas E. Henley. "Confidentiality, privacy, and security of genetic and genomic test information in electronic health records: points to consider." Genetics in Medicine 10, no. 7 (July 2008): 495–99. http://dx.doi.org/10.1097/gim.0b013e31817a8aaa.

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Blanton, Marina, and Fattaneh Bayatbabolghani. "Efficient Server-Aided Secure Two-Party Function Evaluation with Applications to Genomic Computation." Proceedings on Privacy Enhancing Technologies 2016, no. 4 (October 1, 2016): 144–64. http://dx.doi.org/10.1515/popets-2016-0033.

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AbstractComputation based on genomic data is becoming increasingly popular today, be it for medical or other purposes. Non-medical uses of genomic data in a computation often take place in a server-mediated setting where the server offers the ability for joint genomic testing between the users. Undeniably, genomic data is highly sensitive, which in contrast to other biometry types, discloses a plethora of information not only about the data owner, but also about his or her relatives. Thus, there is an urgent need to protect genomic data. This is particularly true when the data is used in computation for what we call recreational non-health-related purposes. Towards this goal, in this work we put forward a framework for server-aided secure two-party computation with the security model motivated by genomic applications. One particular security setting that we treat in this work provides stronger security guarantees with respect to malicious users than the traditional malicious model. In particular, we incorporate certified inputs into secure computation based on garbled circuit evaluation to guarantee that a malicious user is unable to modify her inputs in order to learn unauthorized information about the other user’s data. Our solutions are general in the sense that they can be used to securely evaluate arbitrary functions and offer attractive performance compared to the state of the art. We apply the general constructions to three specific types of genomic tests: paternity, genetic compatibility, and ancestry testing and implement the constructions. The results show that all such private tests can be executed within a matter of seconds or less despite the large size of one’s genomic data.

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Yohe, Sophia L., Alexis B. Carter, John D. Pfeifer, James M. Crawford, Allison Cushman-Vokoun, Samuel Caughron, and Debra G. B. Leonard. "Standards for Clinical Grade Genomic Databases." Archives of Pathology & Laboratory Medicine 139, no. 11 (November 1, 2015): 1400–1412. http://dx.doi.org/10.5858/arpa.2014-0568-cp.

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Context Next-generation sequencing performed in a clinical environment must meet clinical standards, which requires reproducibility of all aspects of the testing. Clinical-grade genomic databases (CGGDs) are required to classify a variant and to assist in the professional interpretation of clinical next-generation sequencing. Applying quality laboratory standards to the reference databases used for sequence-variant interpretation presents a new challenge for validation and curation. Objectives To define CGGD and the categories of information contained in CGGDs and to frame recommendations for the structure and use of these databases in clinical patient care. Design Members of the College of American Pathologists Personalized Health Care Committee reviewed the literature and existing state of genomic databases and developed a framework for guiding CGGD development in the future. Results Clinical-grade genomic databases may provide different types of information. This work group defined 3 layers of information in CGGDs: clinical genomic variant repositories, genomic medical data repositories, and genomic medicine evidence databases. The layers are differentiated by the types of genomic and medical information contained and the utility in assisting with clinical interpretation of genomic variants. Clinical-grade genomic databases must meet specific standards regarding submission, curation, and retrieval of data, as well as the maintenance of privacy and security. Conclusion These organizing principles for CGGDs should serve as a foundation for future development of specific standards that support the use of such databases for patient care.

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Arshad, Saadia, Junaid Arshad, Muhammad Mubashir Khan, and Simon Parkinson. "Analysis of security and privacy challenges for DNA-genomics applications and databases." Journal of Biomedical Informatics 119 (July 2021): 103815. http://dx.doi.org/10.1016/j.jbi.2021.103815.

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Ostrak, Andre, Jaak Randmets, Ville Sokk, Sven Laur, and Liina Kamm. "Implementing Privacy-Preserving Genotype Analysis with Consideration for Population Stratification." Cryptography 5, no. 3 (August 20, 2021): 21. http://dx.doi.org/10.3390/cryptography5030021.

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In bioinformatics, genome-wide association studies (GWAS) are used to detect associations between single-nucleotide polymorphisms (SNPs) and phenotypic traits such as diseases. Significant differences in SNP counts between case and control groups can signal association between variants and phenotypic traits. Most traits are affected by multiple genetic locations. To detect these subtle associations, bioinformaticians need access to more heterogeneous data. Regulatory restrictions in cross-border health data exchange have created a surge in research on privacy-preserving solutions, including secure computing techniques. However, in studies of such scale, one must account for population stratification, as under- and over-representation of sub-populations can lead to spurious associations. We improve on the state of the art of privacy-preserving GWAS methods by showing how to adapt principal component analysis (PCA) with stratification control (EIGENSTRAT), FastPCA, EMMAX and the genomic control algorithm for secure computing. We implement these methods using secure computing techniques—secure multi-party computation (MPC) and trusted execution environments (TEE). Our algorithms are the most complex ones at this scale implemented with MPC. We present performance benchmarks and a security and feasibility trade-off discussion for both techniques.

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Jin, Xiao-Ling, Miao Zhang, Zhongyun Zhou, and Xiaoyu Yu. "Application of a Blockchain Platform to Manage and Secure Personal Genomic Data: A Case Study of LifeCODE.ai in China." Journal of Medical Internet Research 21, no. 9 (September 10, 2019): e13587. http://dx.doi.org/10.2196/13587.

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Background The rapid development of genetic and genomic technologies, such as next-generation sequencing and genome editing, has made disease treatment much more precise and effective. The technologies’ value can only be realized by the aggregation and analysis of people’s genomic and health data. However, the collection and sharing of genomic data has many obstacles, including low data quality, information islands, tampering distortions, missing records, leaking of private data, and gray data transactions. Objective This study aimed to prove that emerging blockchain technology provides a solution for the protection and management of sensitive personal genomic data because of its decentralization, traceability, encryption algorithms, and antitampering features. Methods This paper describes the case of a blockchain-based genomic big data platform, LifeCODE.ai, to illustrate the means by which blockchain enables the storage and management of genomic data from the perspectives of data ownership, data sharing, and data security. Results Blockchain opens up new avenues for dealing with data ownership, data sharing, and data security issues in genomic big data platforms and realizes the psychological empowerment of individuals in the platform. Conclusions The blockchain platform provides new possibilities for the management and security of genetic data and can help realize the psychological empowerment of individuals in the process, and consequently, the effects of data self-governance, incentive-sharing, and security improvement can be achieved. However, there are still some problems in the blockchain that have not been solved, and which require continuous in-depth research and innovation in the future.

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Blanton, Marina, and Fattaneh Bayatbabolghani. "Improving the Security and Efficiency of Private Genomic Computation Using Server Aid." IEEE Security & Privacy 15, no. 5 (2017): 20–28. http://dx.doi.org/10.1109/msp.2017.3681056.

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Tasci, Erdal, Ying Zhuge, Kevin Camphausen, and Andra V. Krauze. "Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets." Cancers 14, no. 12 (June 12, 2022): 2897. http://dx.doi.org/10.3390/cancers14122897.

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Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.

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Burns, Shohei, and Eric Andrew Collisson. "Blockchain-authenticated sharing of cancer patient genomic and clinical outcomes data." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e19358-e19358. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e19358.

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e19358 Background: Efficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of cancer patients to share their treatment experiences to fuel research, despite potential risks to privacy. The objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for dissemination of de-identified clinical and genomic data with a focus on late stage cancer. Methods: We created and piloted a blockchain-authenticated system to enable securely sharing de-identified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHR), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (n = 18), which we uploaded to CGT. EHR data were extracted from both a hospital cancer registry and a common data model format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold-standard source documentation for patients with available data (n = 17). Results: While the total completeness scores were greater for the registry reports than the common data model, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting common data model. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world cancer patient data in a more clinically useful timeframe. We also developed an open-source web application to allow users to seamlessly search, browse, explore, and download CGT data. Conclusions: Our pilot demonstrates the willingness of cancer patients to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.

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Perez-Pozuelo, Ignacio, Dimitris Spathis, Jordan Gifford-Moore, Jessica Morley, and Josh Cowls. "Digital phenotyping and sensitive health data: Implications for data governance." Journal of the American Medical Informatics Association 28, no. 9 (February 27, 2021): 2002–8. http://dx.doi.org/10.1093/jamia/ocab012.

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Abstract In this perspective we want to highlight the rise of what we call “digital phenotyping” or inferring insights about peopleãs health and behavior from their digital devices and data, and the challenges this introduces. Indeed, the collection, processing, and storage of data comes with significant ethical, security and data governance considerations. The COVID-19 pandemic has laid bare the importance of scientific data and modeling, both to understand the nature and spread of the disease, and to develop treatment. But digital devices have also played a (controversial) role, with track and trace systems and increasingly “vaccine passports” being rolled out to help societies open back up. These systems epitomize a wider and longer-standing trend towards seeing almost any form of personal data as potentially health data, especially with the rise of consumer health trackers and other gadgets. Here, we offer an overview of the risks this introduces, drawing on the earlier revolution in genomic sequencing, and propose guidelines to help protect privacy whilst utilizing personal data to help get society back up to speed.

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Husereau, Don, Lotte Steuten, Vivek Muthu, David M. Thomas, Daryl S. Spinner, Craig Ivany, Michael Mengel, et al. "Effective and Efficient Delivery of Genome-Based Testing-What Conditions Are Necessary for Health System Readiness?" Healthcare 10, no. 10 (October 19, 2022): 2086. http://dx.doi.org/10.3390/healthcare10102086.

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Health systems internationally must prepare for a future of genetic/genomic testing to inform healthcare decision-making while creating research opportunities. High functioning testing services will require additional considerations and health system conditions beyond traditional diagnostic testing. Based on a literature review of good practices, key informant interviews, and expert discussion, this article attempts to synthesize what conditions are necessary, and what good practice may look like. It is intended to aid policymakers and others designing future systems of genome-based care and care prevention. These conditions include creating communities of practice and healthcare system networks; resource planning; across-region informatics; having a clear entry/exit point for innovation; evaluative function(s); concentrated or coordinated service models; mechanisms for awareness and care navigation; integrating innovation and healthcare delivery functions; and revisiting approaches to financing, education and training, regulation, and data privacy and security. The list of conditions we propose was developed with an emphasis on describing conditions that would be applicable to any healthcare system, regardless of capacity, organizational structure, financing, population characteristics, standardization of care processes, or underlying culture.

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Schlosberg, Arran. "Data security in genomics: A review of Australian privacy requirements and their relation to cryptography in data storage." Journal of Pathology Informatics 7, no. 1 (2016): 6. http://dx.doi.org/10.4103/2153-3539.175793.

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Tahir, Muhammad, Muhammad Sardaraz, and Usman Aziz. "Critical Review of Blockchain Consensus Algorithms: challenges and opportunities." Vol 4 Issue 5 4, no. 5 (June 30, 2022): 52–64. http://dx.doi.org/10.33411/ijist/2022040505.

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Blockchain is a distributed ledger in which transactions are grouped in blocks linked by hash pointers. Blockchain-based solutions provide trust and privacy because of the resistance to the inconsistency of data and advanced cryptographic features. In various fields, blockchain technology has been implemented to ensure transparency, verifiability, interoperability, governance, and management of information systems. Processing large volumes of data being generated through emerging technologies is a big issue. Many researchers have used Blockchain in various fields integrated with IoT, i.e., industry 4.0, biomedical, health, genomics, etc. Blockchain has the attributes of decentralization, solidness, security, and immutability with a possibility to secure the system design for transmission and storage of data. The purpose of the consensus protocols is to keep up the security and effectiveness of the blockchain network. Utilizing the correct protocol enhances the performance of the blockchain applications. This article presents essential principles and attributes of consensus algorithms to show the applications, challenges, and opportunities of blockchain technology. Moreover, future research directions are also presented to choose an appropriate consensus algorithm to enhance the performance of Blockchain based applications

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A/P Mathiallahan, Puvanamathi, Kaniemozhi A/P Katheravan, and Nabeel Mahdi Althabhawi. "SCIENCE AND TECHNOLOGY'S INFLUENCE ON LAW: THE PERSPECTIVE OF JURISPRUDENCE." Economic Growth and Environment Sustainability 1, no. 2 (2022): 32–35. http://dx.doi.org/10.26480/egnes.02.2022.32.35.

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There are gaps and obstacles in legal practice and administration, including cyber security vulnerabilities, intellectual property issues, negative effects on labour and privacy and data protection concerns. It is vital to understand the function of legislation in governing science and technology, as well as the ethical implications of scientific study and modern technologies. With the advancement of the internet, technology, genomics, telecommunications and other fields of science and technology, legal scholars and law schools have placed a larger emphasis on the intersection of law and science and technology. This article discusses the relationship between law and artificial intelligence and jurisprudence. It explains how law and AI (Artificial Intelligence) are related to jurisprudence in the context of sociology, moral judgments made by AI and the problems that AI faces while making moral judgments. Later in the chapter, a future perspective on AI is discussed. The article focuses on the necessity of AI in law because, while AI offers numerous benefits, like enhanced creativity, services, safety, and lifestyle, it also generates numerous anxiety and concerns about undesirable consequences on human autonomy, privacy and fundamental rights and freedoms.

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Kazi, A. Mujeeb, Niaz Ali, Amir Ibrahim, Abdul Aziz Napar, M. Jamil, Sajjad Hussain, Zahid Mahmood, et al. "Tissue Culture Mediated Allelic Diversification and Genomic Enrichment of Wheat to Combat Production Constraints and Address Food Security." Plant Tissue Culture and Biotechnology 27, no. 1 (December 27, 2017): 89–140. http://dx.doi.org/10.3329/ptcb.v27i1.35018.

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In view of the emerging population trends and that wheat crop is the major unequivocally recognized conduit towards addressing the food security challenges of 2050 this discourse embraces various research options that are viewed as possible solutions toward delivering those targets for providing nutritious food and meeting the aspirations that policy setters have debated on the subject for decades. The underlying strength for achieving these targets will require concerted efforts from plant researchers that are well integrated within effectively harnessing and utilizing prevalent genetic diversity of the wide array of alleles in a holistic pro‐active manner. We argue that the purists of basic and strategic research dimensions need to be thoughtfully defined, so that the vital target of delivering the “applied” gains are only realized from the outputs on farmer’s fields and measured by tons per hectare. In this quest, the pre‐breeding disciplines “classical mode” and its recently surfaced “modified sense” are pivotal, where within the former facet “tissue culture” (TC)/artificial culturing is embodied integrally. Taken for granted, TC has been the backbone of all wide hybridization studies and has made an enormous impact on the agricultural landscape spanning over the last six decades. With its intervention significant generic and specific incompatibilities have been overcome as well as allowing researchers to exploit the protocols for adding efficiency to breeding programs, facilitate operational technologies in running breeding programs and development of unique genetic stocks that preserve valuable allelic richness in user friendly forms for future free germplasmusage in global/private domains of plant improvement ventures.Plant Tissue Cult. & Biotech. 27(1): 89-140, 2017 (June)

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Shamshurina, N. G. "THE SOCIAL PERSPECTIVES OF DIGITIZATION OF HEALTH CARE: THE MEDICAL SOCIOLOGICAL ASPECT." Sociology of Medicine 18, no. 1 (June 15, 2019): 50–54. http://dx.doi.org/10.18821/1728-2810-2019-18-1-50-54.

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In the Russian Federation, the technology platform «Medicine of the future» is specifically identified in the sector of digital economics. In the «Medicine and health care» sector, diagnostic systems based on molecular and cellular targets, genomic, post-genomic and cellular technologies are singled out. In the «Pharmaceutical industry" sector, the breakthrough areas include such innovative pharmaceuticals as vaccines (DNA vaccines), hormonal agents, coagulation factors, drugs based on cytokines, monoclonal antibodies, drugs for demographically significant diseases, antiseptics. In the sector of «Production of new materials» the priority targets is development of nano-technology and nano-materials and technologies of elaboration of bio-compatible materials. The main target is training and retraining of personnel in digital medicine skills, organization of national technological platforms for on-line education, on-line medicine and adjustment of existing and development of new educational programs. The social alterations resulting due to health care digitalization are associated with transformation of the structure of labor market of medical personnel, giving rise to emergence of new medical professions at the scientific research junction. The social perspectives of health care digitalization reflect formation of «knowledge society», development of information society and digital economics as a whole, development of competitive technologies and services in medicine. The world expert community, implementing sociological and socio-economic research, confirms that digitalization of medicine and economics gave rise to the ideology of «Social Investment» («Impact Investing», or, otherwise, «investment in social effect»). The digitalization of health care and economics has led to the need of developing new civilizational paradigm, to the necessity of human dimension of technological and economic processes. The positive social changes caused by development of digital medicine, do not exclude the emergence of social risks, manifested in possible violation of privacy, patients' rights, reducing level of security and dangerous dehumanization of society, reducing the value of patient as individual in conditions of development of biomedicine and genetic engineering as areas of digital medicine.

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Mott, Richard, Christian Fischer, Pjotr Prins, and Robert William Davies. "Private Genomes and Public SNPs: Homomorphic Encryption of Genotypes and Phenotypes for Shared Quantitative Genetics." Genetics 215, no. 2 (April 23, 2020): 359–72. http://dx.doi.org/10.1534/genetics.120.303153.

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Sharing human genotype and phenotype data is essential to discover otherwise inaccessible genetic associations, but is a challenge because of privacy concerns. Here, we present a method of homomorphic encryption that obscures individuals’ genotypes and phenotypes, and is suited to quantitative genetic association analysis. Encrypted ciphertext and unencrypted plaintext are analytically interchangeable. The encryption uses a high-dimensional random linear orthogonal transformation key that leaves the likelihood of quantitative trait data unchanged under a linear model with normally distributed errors. It also preserves linkage disequilibrium between genetic variants and associations between variants and phenotypes. It scrambles relationships between individuals: encrypted genotype dosages closely resemble Gaussian deviates, and can be replaced by quantiles from a Gaussian with negligible effects on accuracy. Likelihood-based inferences are unaffected by orthogonal encryption. These include linear mixed models to control for unequal relatedness between individuals, heritability estimation, and including covariates when testing association. Orthogonal transformations can be applied in a modular fashion for multiparty federated mega-analyses where the parties first agree to share a common set of genotype sites and covariates prior to encryption. Each then privately encrypts and shares their own ciphertext, and analyses all parties’ ciphertexts. In the absence of private variants, or knowledge of the key, we show that it is infeasible to decrypt ciphertext using existing brute-force or noise-reduction attacks. We present the method as a challenge to the community to determine its security.

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Mokhov, A. A. "Genome Registration in Russia: Problems and Prospects of Development." Actual Problems of Russian Law 15, no. 7 (August 7, 2020): 103–13. http://dx.doi.org/10.17803/1994-1471.2020.116.7.103-113.

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Evolving genetic technologies influence various social relations: medical care, biosafety, crime control, etc. Genome registration originated as a part of forensic identification of a person and now it is actively developing. Thus, we are facing with a set of organizational, financial and legal problems that need to be resolved. The current legislation in terms of genome registration is mainly focused on solving problems of crime prevention and crime control. Therefore, its potential is almost exhausted. The authors suggest that the preocedure of voluntary genome registration should be developed, which will facilitate resolution of problems arising not only with regard to forensics, but also in biomedicine and other areas of life. In this regard, the paper argues that it is necessary to establish a universal government database of genomic data and focuses on a number of questions raised in connection with genetic passportization, namely: citizens’ rights protection and data security. According to the author, the expansion of the range of tasks that genome registration can complete will give impetus to the development of this activity. However, it is necessary to solve, in a timely manner, a set of interrelated legal, organizational, financial problems, to ensure a balance between private and public interests, biological and data security.

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Allen, Caitlin G., Leslie Lenert, Kelly Hunt, Amy Jackson, Elissa Levin, Catherine Clinton, John T. Clark, et al. "Lessons Learned from the Pilot Phase of a Population-Wide Genomic Screening Program: Building the Base to Reach a Diverse Cohort of 100,000 Participants." Journal of Personalized Medicine 12, no. 8 (July 27, 2022): 1228. http://dx.doi.org/10.3390/jpm12081228.

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Background and Objectives: Genomic information is increasingly relevant for disease prevention and risk management at the individual and population levels. Screening healthy adults for Tier 1 conditions of hereditary breast and ovarian cancer, Lynch syndrome, and familial hypercholesterolemia using a population-based approach can help identify the 1–2% of the US population at increased risk of developing diseases associated with these conditions and tailor prevention strategies. Our objective is to report findings from an implementation science study that evaluates multi-level facilitators and barriers to implementation of the In Our DNA SC population-wide genomic screening initiative. Methods: We established an IMPACTeam (IMPlementAtion sCience for In Our DNA SC Team) to evaluate the pilot phase using principles of implementation science. We used a parallel convergent mixed methods approach to assess the Reach, Implementation, and Effectiveness outcomes from the RE-AIM implementation science framework during the pilot phase of In Our DNA SC. Quantitative assessment included the examination of frequencies and response rates across demographic categories using chi-square tests. Qualitative data were audio-recorded and transcribed, with codes developed by the study team based on the semi-structured interview guide. Results: The pilot phase (8 November 2021, to 7 March 2022) included recruitment from ten clinics throughout South Carolina. Reach indicators included enrollment rate and representativeness. A total of 23,269 potential participants were contacted via Epic’s MyChart patient portal with 1976 (8.49%) enrolled. Black individuals were the least likely to view the program invitation (28.9%) and take study-related action. As a result, there were significantly higher enrollment rates among White (10.5%) participants than Asian (8.71%) and Black (3.46%) individuals (p < 0.0001). Common concerns limiting reach and participation included privacy and security of results and the impact participation would have on health or life insurance. Facilitators included family or personal history of a Tier 1 condition, prior involvement in genetic testing, self-interest, and altruism. Assessment of implementation (i.e., adherence to protocols/fidelity to protocols) included sample collection rate (n = 1104, 55.8%) and proportion of samples needing recollection (n = 19, 1.7%). There were no significant differences in sample collection based on demographic characteristics. Implementation facilitators included efficient collection processes and enthusiastic clinical staff. Finally, we assessed the effectiveness of the program, finding low dropout rates (n = 7, 0.35%), the identification of eight individuals with Tier 1 conditions (0.72% positive), and high rates of follow-up genetic counseling (87.5% completion). Conclusion: Overall, Asian and Black individuals were less engaged, with few taking any study-related actions. Strategies to identify barriers and promoters for the engagement of diverse populations are needed to support participation. Once enrolled, individuals had high rates of completing the study and follow-up engagement with genetic counselors. Findings from the pilot phase of In Our DNA SC offer opportunities for improvement as we expand the program and can provide guidance to organizations seeking to begin efforts to integrate population-wide genomic screening.

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Bird, Stephanie J. "Security and Privacy: Why Privacy Matters." Science and Engineering Ethics 19, no. 3 (July 27, 2013): 669–71. http://dx.doi.org/10.1007/s11948-013-9458-z.

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Guynes, Carl S., Grichard G. Vedder, and Michael T. Vanecek. "Privacy and security." ACM SIGCAS Computers and Society 26, no. 1 (March 1996): 11–13. http://dx.doi.org/10.1145/229403.229409.

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Walters, Gregory J. "Privacy and security." ACM SIGCAS Computers and Society 31, no. 2 (June 2001): 8–23. http://dx.doi.org/10.1145/503345.503347.

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Milojicic, D. "Security and privacy." IEEE Concurrency 8, no. 2 (April 2000): 70–79. http://dx.doi.org/10.1109/mcc.2000.846196.

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Kenneally, Erin. "Privacy and Security." IEEE Internet of Things Magazine 1, no. 1 (September 2018): 8–10. http://dx.doi.org/10.1109/miot.2018.8552484.

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Pujals, Joseph M. "Security and privacy." Computers & Security 12, no. 1 (February 1993): 22–27. http://dx.doi.org/10.1016/0167-4048(93)90005-p.

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O’Brien, Ralph. "Privacy and security." Business Information Review 33, no. 2 (June 2016): 81–84. http://dx.doi.org/10.1177/0266382116650297.

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Jasny, Barbara R. "Genomic crowdsourcing with privacy." Science 360, no. 6393 (June 7, 2018): 1083.6–1084. http://dx.doi.org/10.1126/science.360.6393.1083-f.

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Ireni-Saban, Liza. "Genomics Governance in the United States and the United Kingdom." European Journal of Comparative Law and Governance 1, no. 3 (July 13, 2014): 244–65. http://dx.doi.org/10.1163/22134514-00103003.

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Technological innovation in the area of personalised genetic data poses novel regulatory concerns for state governance. Since personalised genetic data reveals highly sensitive and private information about a person’s susceptibility to illness, it may lead to stigmatisation, discrimination, and breach of privacy. Although legal arrangements for personal or medical data have always been governmental and legal concerns, the introduction of genetic technologies over the past two decades has breathed new life into the idea of privacy and non-discrimination protection for individuals and communities, leading to possible new types of social relationships that circulate in a global biomedical arena. Thus, our analysis of genetic information regulation is based on a comparative analysis of policy instruments by examining the appropriateness of various policy instrument choices made in the United States and in the United Kingdom for securing the rights for privacy, non-discrimination, and access to research benefits for individuals and communities.

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Gostin, Lawrence O. "Genetic Privacy." Journal of Law, Medicine & Ethics 23, no. 4 (1995): 320–30. http://dx.doi.org/10.1111/j.1748-720x.1995.tb01374.x.

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Human genomic information is invested with enormous power in a scientifically motivated society. Genomic information has the capacity to produce a great deal of good for society. It can help identify and understand the etiology and pathophysiology of disease. In so doing, medicine and science can expand the ability to prevent and ameliorate human malady through genetic testing, treatment, and reproductive counseling.Genomic information can just as powerfully serve less beneficent ends. Information can be used to discover deeply personal attributes of an individual's life. That information can be used to invade a person's private sphere, to alter a person's sense of self- and family identity, and to affect adversely opportunities in education, employment, and insurance. Genomic information can also affect families and ethnic groups that share genetic similarities.

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Kumar, Ritik, Arjunaditya, Divyangi Singh, Kathiravan Srinivasan, and Yuh-Chung Hu. "AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions." Healthcare 11, no. 1 (December 27, 2022): 81. http://dx.doi.org/10.3390/healthcare11010081.

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Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.

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Lever, Annabelle. "Privacy, democracy, and security." Philosophers' Magazine, no. 63 (2013): 99–105. http://dx.doi.org/10.5840/tpm201363133.

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Hwang, Seoyeon, Ercan Ozturk, and Gene Tsudik. "Balancing Security and Privacy in Genomic Range Queries*." ACM Transactions on Privacy and Security, December 9, 2022. http://dx.doi.org/10.1145/3575796.

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Exciting recent advances in genome sequencing, coupled with greatly reduced storage and computation costs, make genomic testing increasingly accessible to individuals. Already today, one’s digitized DNA can be easily obtained from a sequencing lab and later used to conduct numerous tests by engaging with a testing facility. Due to the inherent sensitivity of genetic material and the often-proprietary nature of genomic tests, privacy is a natural and crucial issue. While genomic privacy received a great deal of attention within and outside the research community, genomic security has not been sufficiently studied. This is surprising since the usage of fake or altered genomes can have grave consequences, such as erroneous drug prescriptions and genetic test outcomes. Unfortunately, in the genomic domain, privacy and security (as often happens) are at odds with each other. In this paper, we attempt to reconcile security with privacy in genomic testing by designing a novel technique for a secure and private genomic range query protocol between a genomic testing facility and an individual user. The proposed technique ensures authenticity and completeness of user-supplied genomic material while maintaining its privacy by releasing only the minimum thereof. To confirm its broad usability, we show how to apply the proposed technique to a previously proposed genomic private substring matching protocol. Experiments show that the proposed technique offers good performance and is quite practical. Furthermore, we generalize the genomic range query problem to sparse integer sets and discuss potential use cases.

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Alsaffar, Muhalb M., Mohammad Hasan, Gavin P. McStay, and Mohamed Sedky. "Digital DNA lifecycle security and privacy: an overview." Briefings in Bioinformatics 23, no. 2 (January 31, 2022). http://dx.doi.org/10.1093/bib/bbab607.

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Abstract DNA sequencing technologies have advanced significantly in the last few years leading to advancements in biomedical research which has improved personalised medicine and the discovery of new treatments for diseases. Sequencing technology advancement has also reduced the cost of DNA sequencing, which has led to the rise of direct-to-consumer (DTC) sequencing, e.g. 23andme.com, ancestry.co.uk, etc. In the meantime, concerns have emerged over privacy and security in collecting, handling, analysing and sharing DNA and genomic data. DNA data are unique and can be used to identify individuals. Moreover, those data provide information on people’s current disease status and disposition, e.g. mental health or susceptibility for developing cancer. DNA privacy violation does not only affect the owner but also affects their close consanguinity due to its hereditary nature. This article introduces and defines the term ‘digital DNA life cycle’ and presents an overview of privacy and security threats and their mitigation techniques for predigital DNA and throughout the digital DNA life cycle. It covers DNA sequencing hardware, software and DNA sequence pipeline in addition to common privacy attacks and their countermeasures when DNA digital data are stored, queried or shared. Likewise, the article examines DTC genomic sequencing privacy and security.

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Journal articles on the topic 'Genomic security and privacy'

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