Relevant bibliographies by topics / Genomic data security

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Genomic data security'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Genomic data security"

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Vavekanand, Raja. "Data Security and Privacy in Genomics Research: A Comparative Analysis to Protect Confidentiality." Studies in Medical and Health Sciences 1, no. 1 (2024): 23–31. http://dx.doi.org/10.48185/smhs.v1i1.1158.

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The quick progress of genomics examination has driven a surge in the creation of significantly fragile genomic data, making ensuring its security essential. This data contains sensitive information roughly an individual's prosperity, family history, and defencelessness to ailments. Unauthorized access or mishandling can lead to isolation, stigmatization, and mystery breaches. The potential threats to genomic data affirmation are multifaceted, checking the chance of re-identification and extended defense lessness to data breaches, hacking events, and unauthorized get to by harmful actors. To ad
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Smith, Marcus, and Ausma Bernot. "Government and Commercial Interests in Genomics: Improving Data Security and Regulation." Law, Technology and Humans 6, no. 1 (2024): 88–100. http://dx.doi.org/10.5204/lthj.3256.

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The relationship between new technologies and security is well established in the fields of defence, law enforcement, communications and public health. This has been highlighted by recent public debate about the security implications of data held by companies operating in social media and information technology (such as TikTok and Huawei). While genomic technology had been less high profile in the context of security, this changed following the COVID-19 pandemic, which focused attention on the significant implications of this form of data. This article discusses commercial genomic technology,
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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 (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
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P., Shobha, and Nalini N. "Genomic Data Fusion using Paillier Cryptosystem." Journal of Current Science and Technology 14, no. 3 (2024): 57. http://dx.doi.org/10.59796/jcst.v14n3.2024.57.

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The proposed work performs secure data fusion using homomorphic encryption, specifically the Paillier cryptosystem. The Paillier cryptosystem allows computation to be performed on encrypted data without decrypting it first, thus ensuring the privacy and security of the computation. The experiment measures the algorithm's performance based on execution time, memory usage, security, accuracy, and scalability. The data-level Paillier cryptosystem approach is generally slower than the feature-level fusion method due to its more complex operations and computations. Scalability is limited by the tim
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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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Prodduturi, Viswaketan Reddy. "GENOMIC DATA SECURITY AND PRIVACY IN HEALTHCARE INFORMATICS." INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY 8, no. 1 (2025): 563–73. https://doi.org/10.34218/ijrcait_08_01_044.

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Yeh, Kenneth, Jeanne Fair, Helen Cui, et al. "Achieving Health Security and Threat Reduction through Sharing Sequence Data." Tropical Medicine and Infectious Disease 4, no. 2 (2019): 78. http://dx.doi.org/10.3390/tropicalmed4020078.

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With the rapid development and broad applications of next-generation sequencing platforms and bioinformatic analytical tools, genomics has become a popular area for biosurveillance and international scientific collaboration. Governments from countries including the United States (US), Canada, Germany, and the United Kingdom have leveraged these advancements to support international cooperative programs that aim to reduce biological threats and build scientific capacity worldwide. A recent conference panel addressed the impacts of the enhancement of genomic sequencing capabilities through three
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Venkata, Murali Krishna Neursu, Kilaru Kalyan, and Reddy Vatti Vineeth. "Genomic Data Engineering: AI-Enhanced Storage, Processing, and Analysis for Biotechnology Innovations." Global Journal of Engineering and Technology [GJET] 4, no. 2 (2025): 10–12. https://doi.org/10.5281/zenodo.14964119.

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<em>The field of genomic data engineering has been revolutionized by artificial intelligence (AI), enabling efficient storage, processing, and analysis of massive biological datasets. AI-driven techniques enhance the accuracy of genome sequencing, accelerate biomedical research, and facilitate personalized medicine. However, managing and processing genomic data presents challenges related to computational complexity, data security, and scalability. This research explores AI-based methods for optimizing genomic data storage, processing pipelines, and predictive analytics. The study highlights t
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Ammar, Alzaydi, Abedalrhman Kahtan, Nurhaliza Siti, and Ismail Mohd. "Enhancing Cyber Defense Mechanisms for Genomic Data in Personalized Healthcare Systems." Applied Science and Biotechnology Journal for Advanced Research 3, no. 5 (2024): 20–30. https://doi.org/10.5281/zenodo.13852606.

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In the era of personalized medicine, genomic data emerges as a cornerstone for tailored healthcare solutions, offering unprecedented opportunities for disease prediction and prevention. However, this sensitive data is increasingly vulnerable to cyber threats that compromise patient privacy and system integrity. Addressing this critical issue, our research introduces a novel cybersecurity framework specifically designed to protect genomic information within healthcare systems. We develop and implement advanced cryptographic methods, real-time intrusion detection systems, and secure data sharing
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Gudodagi, Raveendra, and R. Venkata Siva Reddy. "Security Provisioning and Compression of Diverse Genomic Data based on Advanced Encryption Standard (AES) Algorithm." International Journal of Biology and Biomedical Engineering 15 (May 14, 2021): 104–12. http://dx.doi.org/10.46300/91011.2021.15.14.

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Compression of genomic data has gained enormous momentum in recent years because of advances in technology, exponentially growing health concerns, and government funding for research. Such advances have driven us to personalize public health and medical care. These pose a considerable challenge for ubiquitous computing in data storage. One of the main issues faced by genomic laboratories is the 'cost of storage' due to the large data file of the human genome (ranging from 30 GB to 200 GB). Data preservation is a set of actions meant to protect data from unauthorized access or changes. There ar
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Dissertations / Theses on the topic "Genomic data security"

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Niyitegeka, David. "Composition de mécanismes cryptographiques et de tatouage pour la protection de données génétiques externalisées." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0225.

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De nos jours, le “cloud computing” permet de mutualiser et de traiter de grandes quantités de données génétiques à un coût minime et sans avoir à maintenir une infrastructure propre. Ces données sont notamment utilisées dans des études d'association pangénomiques (“Genome Wide Association Studies” ou GWAS) afin d’identifier des variants génétiques associées à certaines maladies. Cependant, leur externalisation induit de nombreux problèmes en matière de sécurité. Notamment, le génome humain représente l'unique identité biologique d’un individu et est donc par nature une information très sensibl
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Book chapters on the topic "Genomic data security"

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Humbert, Mathias, Erman Ayday, Jean-Pierre Hubaux, and Amalio Telenti. "On Non-cooperative Genomic Privacy." In Financial Cryptography and Data Security. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47854-7_24.

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Ayday, Erman. "Cryptographic Solutions for Genomic Privacy." In Financial Cryptography and Data Security. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-53357-4_22.

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Ayday, Erman, Jean Louis Raisaro, Urs Hengartner, Adam Molyneaux, and Jean-Pierre Hubaux. "Privacy-Preserving Processing of Raw Genomic Data." In Data Privacy Management and Autonomous Spontaneous Security. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54568-9_9.

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Yamamoto, Akito, and Tetsuo Shibuya. "Privacy-Preserving Genomic Statistical Analysis Under Local Differential Privacy." In Data and Applications Security and Privacy XXXVII. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37586-6_3.

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Zhao, Chuan, Shengnan Zhao, Bo Zhang, Shan Jing, Zhenxiang Chen, and Minghao Zhao. "Towards Secure Computation of Similar Patient Query on Genomic Data Under Multiple Keys." In Cyberspace Safety and Security. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37352-8_24.

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Vasanth, R., and Dinesh Jackson Samuel. "Providing Data Security in Deep Learning by Using Genomic Procedure." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0199-9_22.

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Teruya, Tadanori, Koji Nuida, Kana Shimizu, and Goichiro Hanaoka. "On Limitations and Alternatives of Privacy-Preserving Cryptographic Protocols for Genomic Data." In Advances in Information and Computer Security. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22425-1_15.

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Chen, Jing, Zhiping Chen, Linai Kuang, et al. "Security Count Query and Integrity Verification Based on Encrypted Genomic Data." In Proceedings of the 9th International Conference on Computer Engineering and Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3753-0_63.

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Habyarimana, Ephrem. "Future Vision, Summary and Outlook." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_21.

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AbstractThe DataBio’s agriculture pilots were carried out through a multi-actor whole-farm management approach using information technology, satellite positioning and remote sensing data as well as Internet of Things technology. The goal was to optimize the returns on inputs while reducing environmental impacts and streamlining the CAP monitoring. Novel knowledge was delivered for a more sustainable agriculture in line with the FAO call to achieve global food security and eliminate malnutrition for the more than nine billion people by 2050. The findings from the pilots shed light on the potent
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Pálhalmi, János, and Anna Mező. "AI-Powered Microscopy Platform for Airborne Biothreat Detection." In Security Informatics and Law Enforcement. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62083-6_10.

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AbstractBecause Bacillus anthracis is one of the most lethal bioweapons, it is critical to create rapid, label-free screening and early warning systems to detect and classify anomalies in bacillus form vegetative cell and spore concentrations in the air. Even though significant effort has been invested in the development of various sensor solutions to detect, monitor, and identify airborne biological agents, no standard, interoperable, real-time or near-real-time optical sensor-based biothreat monitoring solution exists. Aside from the numerous advantages of genomic methods in microbe identifi
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Conference papers on the topic "Genomic data security"

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Naveed, Muhammad. "Hurdles for Genomic Data Usage Management." In 2014 IEEE Security and Privacy Workshops (SPW). IEEE, 2014. http://dx.doi.org/10.1109/spw.2014.44.

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Goodrich, Michael T. "The Mastermind Attack on Genomic Data." In 2009 30th IEEE Symposium on Security and Privacy (SP). IEEE, 2009. http://dx.doi.org/10.1109/sp.2009.4.

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Oprisanu, Bristena, Georgi Ganev, and Emiliano De Cristofaro. "On Utility and Privacy in Synthetic Genomic Data." In Network and Distributed System Security Symposium. Internet Society, 2022. http://dx.doi.org/10.14722/ndss.2022.24092.

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Huang, Zhicong, Erman Ayday, Jacques Fellay, Jean-Pierre Hubaux, and Ari Juels. "GenoGuard: Protecting Genomic Data against Brute-Force Attacks." In 2015 IEEE Symposium on Security and Privacy (SP). IEEE, 2015. http://dx.doi.org/10.1109/sp.2015.34.

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Cheng, Ke, Yantian Hou, and Liangmin Wang. "Secure Similar Sequence Query on Outsourced Genomic Data." In ASIA CCS '18: ACM Asia Conference on Computer and Communications Security. ACM, 2018. http://dx.doi.org/10.1145/3196494.3196535.

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Migliore, Andrea, Stelvio Cimato, and Gabriella Trucco. "Efficient Secure Computation of Edit Distance on Genomic Data." In 10th International Conference on Information Systems Security and Privacy. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012459400003648.

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Yilmaz, Emre, Tianxi Ji, Erman Ayday, and Pan Li. "Genomic Data Sharing under Dependent Local Differential Privacy." In CODASPY '22: Twelveth ACM Conference on Data and Application Security and Privacy. ACM, 2022. http://dx.doi.org/10.1145/3508398.3511519.

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Simmons, Sean, and Bonnie Berger. "One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data." In 2015 IEEE Security and Privacy Workshops (SPW). IEEE, 2015. http://dx.doi.org/10.1109/spw.2015.25.

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Ki, Youngjoon, and Ji Won Yoon. "An Efficient Method for Securely Storing and Handling of Genomic Data." In 2017 International Conference on Software Security and Assurance (ICSSA). IEEE, 2017. http://dx.doi.org/10.1109/icssa.2017.13.

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Turkmen, Fatih, Muhammad Rizwan Asghar, and Yuri Demchenko. "iGenoPri: Privacy-preserving genomic data processing with integrity and correctness proofs." In 2016 14th Annual Conference on Privacy, Security and Trust (PST). IEEE, 2016. http://dx.doi.org/10.1109/pst.2016.7906964.

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Reports on the topic "Genomic data security"

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Verzi, Stephen, Raga Krishnakumar, Drew Levin, Daniel Krofcheck, and Kelly Williams. Data Science and Machine Learning for Genome Security. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1855003.

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Fromm, Hillel, Paul Michael Hasegawa, and Aaron Fait. Calcium-regulated Transcription Factors Mediating Carbon Metabolism in Response to Drought. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7699847.bard.

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Original objectives: The long-term goal of the proposed research is to elucidate the transcription factors, genes and metabolic networks involved in carbon metabolism and partitioning in response to water deficit. The proposed research focuses on the GTLcalcium/calmodulinbindingTFs and the gene and metabolic networks modulated by these TFs in Arabidopsis thaliana. The specific objectives are as follows. Objective-1 (USA): Physiological analyses of GTL1 loss- and gain-of-function plants under water sufficient and drought stress conditions Objective 2 (USA / Israel-TAU): Characterizion of GTL ta
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Lers, Amnon, Majid R. Foolad, and Haya Friedman. genetic basis for postharvest chilling tolerance in tomato fruit. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7600014.bard.

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ABSTRACT Postharvest losses of fresh produce are estimated globally to be around 30%. Reducing these losses is considered a major solution to ensure global food security. Storage at low temperatures is an efficient practice to prolong postharvest performance of crops with minimal negative impact on produce quality or human health and the environment. However, many fresh produce commodities are susceptible to chilling temperatures, and the application of cold storage is limited as it would cause physiological chilling injury (CI) leading to reduced produce quality. Further, the primary CI becom
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