1
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Ye F, Cho H, Rouayheb SE. Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy. IEEE TRANSACTIONS ON INFORMATION THEORY 2022; 68:4090-4105. [PMID: 37283781 PMCID: PMC10243750 DOI: 10.1109/tit.2022.3156276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Motivated by the growing availability of personal genomics services, we study an information-theoretic privacy problem that arises when sharing genomic data: a user wants to share his or her genome sequence while keeping the genotypes at certain positions hidden, which could otherwise reveal critical health-related information. A straightforward solution of erasing (masking) the chosen genotypes does not ensure privacy, because the correlation between nearby positions can leak the masked genotypes. We introduce an erasure-based privacy mechanism with perfect information-theoretic privacy, whereby the released sequence is statistically independent of the sensitive genotypes. Our mechanism can be interpreted as a locally-optimal greedy algorithm for a given processing order of sequence positions, where utility is measured by the number of positions released without erasure. We show that finding an optimal order is NP-hard in general and provide an upper bound on the optimal utility. For sequences from hidden Markov models, a standard modeling approach in genetics, we propose an efficient algorithmic implementation of our mechanism with complexity polynomial in sequence length. Moreover, we illustrate the robustness of the mechanism by bounding the privacy leakage from erroneous prior distributions. Our work is a step towards more rigorous control of privacy in genomic data sharing.
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Affiliation(s)
- Fangwei Ye
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hyunghoon Cho
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Salim El Rouayheb
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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2
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Communication-efficient federated learning via knowledge distillation. Nat Commun 2022; 13:2032. [PMID: 35440643 PMCID: PMC9018897 DOI: 10.1038/s41467-022-29763-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/31/2022] [Indexed: 12/12/2022] Open
Abstract
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw data. However, model updates can be extremely large if they contain numerous parameters, and many rounds of communication are needed for model training. The huge communication cost in federated learning leads to heavy overheads on clients and high environmental burdens. Here, we present a federated learning method named FedKD that is both communication-efficient and effective, based on adaptive mutual knowledge distillation and dynamic gradient compression techniques. FedKD is validated on three different scenarios that need privacy protection, showing that it maximally can reduce 94.89% of communication cost and achieve competitive results with centralized model learning. FedKD provides a potential to efficiently deploy privacy-preserving intelligent systems in many scenarios, such as intelligent healthcare and personalization. This work presents a communication-efficient federated learning method that saves a major fraction of communication cost. It reveals the advantage of reciprocal learning in machine knowledge transfer and the evolutional low-rank properties of deep model updates.
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3
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Wan Z, Hazel JW, Clayton EW, Vorobeychik Y, Kantarcioglu M, Malin BA. Sociotechnical safeguards for genomic data privacy. Nat Rev Genet 2022; 23:429-445. [PMID: 35246669 PMCID: PMC8896074 DOI: 10.1038/s41576-022-00455-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/24/2022] [Indexed: 12/21/2022]
Abstract
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. In this Review, the authors describe technical and legal protection mechanisms for mitigating vulnerabilities in genomic data privacy. They also discuss how these protections are dependent on the context of data use such as in research, health care, direct-to-consumer testing or forensic investigations.
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Affiliation(s)
- Zhiyu Wan
- Center for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Computer Science, Vanderbilt University, Nashville, TN, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - James W Hazel
- Center for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center, Nashville, TN, USA.,Center for Biomedical Ethics and Society, Vanderbilt University, Nashville, TN, USA
| | - Ellen Wright Clayton
- Center for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center, Nashville, TN, USA.,Center for Biomedical Ethics and Society, Vanderbilt University, Nashville, TN, USA.,Vanderbilt University Law School, Nashville, TN, USA
| | - Yevgeniy Vorobeychik
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Murat Kantarcioglu
- Department of Computer Science, University of Texas at Dallas, Richardson, TX, USA
| | - Bradley A Malin
- Center for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center, Nashville, TN, USA. .,Department of Computer Science, Vanderbilt University, Nashville, TN, USA. .,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. .,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
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4
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Alsaffar MM, Hasan M, McStay GP, Sedky M. Digital DNA lifecycle security and privacy: an overview. Brief Bioinform 2022; 23:6518049. [PMID: 35106557 DOI: 10.1093/bib/bbab607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/29/2021] [Accepted: 12/30/2021] [Indexed: 11/14/2022] Open
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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Affiliation(s)
- Muhalb M Alsaffar
- Department of Computing, AI and Robotics, School of Digital, Technologies and Arts, Staffordshire University, College Road, ST4 2DE, Staffordshire, United Kingdom
| | | | - Gavin P McStay
- Department of Biological Sciences, School of Health, Science and Wellbeing, Staffordshire University, College Road, Stoke-on-Trent, Staffordshire, ST4 2DE, United Kingdom
| | - Mohamed Sedky
- Department of Computing, AI and Robotics, School of Digital, Technologies and Arts, Staffordshire University, College Road, ST4 2DE, Staffordshire, United Kingdom
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5
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Wan Z, Vorobeychik Y, Xia W, Liu Y, Wooders M, Guo J, Yin Z, Clayton EW, Kantarcioglu M, Malin BA. Using game theory to thwart multistage privacy intrusions when sharing data. SCIENCE ADVANCES 2021; 7:eabe9986. [PMID: 34890225 PMCID: PMC8664254 DOI: 10.1126/sciadv.abe9986] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
Person-specific biomedical data are now widely collected, but its sharing raises privacy concerns, specifically about the re-identification of seemingly anonymous records. Formal re-identification risk assessment frameworks can inform decisions about whether and how to share data; current techniques, however, focus on scenarios where the data recipients use only one resource for re-identification purposes. This is a concern because recent attacks show that adversaries can access multiple resources, combining them in a stage-wise manner, to enhance the chance of an attack’s success. In this work, we represent a re-identification game using a two-player Stackelberg game of perfect information, which can be applied to assess risk, and suggest an optimal data sharing strategy based on a privacy-utility tradeoff. We report on experiments with large-scale genomic datasets to show that, using game theoretic models accounting for adversarial capabilities to launch multistage attacks, most data can be effectively shared with low re-identification risk.
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Affiliation(s)
- Zhiyu Wan
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Yevgeniy Vorobeychik
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Weiyi Xia
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Yongtai Liu
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
| | - Myrna Wooders
- Department of Economics, Vanderbilt University, Nashville, TN 37235, USA
| | - Jia Guo
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
| | - Zhijun Yin
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Ellen Wright Clayton
- Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN 37203, USA
- School of Law, Vanderbilt University, Nashville, TN 37203, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Murat Kantarcioglu
- Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA
- Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Bradley A. Malin
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
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6
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Öksüz AÇ, Ayday E, Güdükbay U. Privacy-preserving and robust watermarking on sequential genome data using belief propagation and local differential privacy. Bioinformatics 2021; 37:2668-2674. [PMID: 33630065 PMCID: PMC11025661 DOI: 10.1093/bioinformatics/btab128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/09/2021] [Accepted: 02/23/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Genome data is a subject of study for both biology and computer science since the start of the Human Genome Project in 1990. Since then, genome sequencing for medical and social purposes becomes more and more available and affordable. Genome data can be shared on public websites or with service providers (SPs). However, this sharing compromises the privacy of donors even under partial sharing conditions. We mainly focus on the liability aspect ensued by the unauthorized sharing of these genome data. One of the techniques to address the liability issues in data sharing is the watermarking mechanism. RESULTS To detect malicious correspondents and SPs-whose aim is to share genome data without individuals' consent and undetected-, we propose a novel watermarking method on sequential genome data using belief propagation algorithm. In our method, we have two criteria to satisfy. (i) Embedding robust watermarks so that the malicious adversaries cannot temper the watermark by modification and are identified with high probability. (ii) Achieving ϵ-local differential privacy in all data sharings with SPs. For the preservation of system robustness against single SP and collusion attacks, we consider publicly available genomic information like Minor Allele Frequency, Linkage Disequilibrium, Phenotype Information and Familial Information. Our proposed scheme achieves 100% detection rate against the single SP attacks with only 3% watermark length. For the worst case scenario of collusion attacks (50% of SPs are malicious), 80% detection is achieved with 5% watermark length and 90% detection is achieved with 10% watermark length. For all cases, the impact of ϵ on precision remained negligible and high privacy is ensured. AVAILABILITY AND IMPLEMENTATION https://github.com/acoksuz/PPRW\_SGD\_BPLDP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Erman Ayday
- Department of Computer Engineering, Bilkent University, Ankara, Turkey
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA
| | - Uğur Güdükbay
- Department of Computer Engineering, Bilkent University, Ankara, Turkey
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7
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Mann M, Kumar C, Zeng WF, Strauss MT. Artificial intelligence for proteomics and biomarker discovery. Cell Syst 2021; 12:759-770. [PMID: 34411543 DOI: 10.1016/j.cels.2021.06.006] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/07/2021] [Accepted: 06/28/2021] [Indexed: 12/14/2022]
Abstract
There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings.
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Affiliation(s)
- Matthias Mann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
| | - Chanchal Kumar
- Translational Science & Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
| | - Wen-Feng Zeng
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
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8
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Racine V. Can Blockchain Solve the Dilemma in the Ethics of Genomic Biobanks? SCIENCE AND ENGINEERING ETHICS 2021; 27:35. [PMID: 34061257 DOI: 10.1007/s11948-021-00311-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 05/03/2021] [Indexed: 06/12/2023]
Abstract
In discussions on the ethics of genome collections and biobanks, the main worry about whether we are permitted to collect and use individuals' genomic and genetic data is the potential for the violation of individuals' right to informational privacy. Yet, if we do not permit these endeavors, we risk giving up on the future benefits of biomedical research. In this paper, I describe a private venture in blockchain genomics that seeks to provide an apt solution to concerns about potential privacy violations in genome collections and biobanks. I then provide some reasons to doubt the kind of solution to the dilemma that such start-ups propose. I argue that the sort of autonomy that grounds the value of the right to informational privacy cannot be secured with blockchain technology alone. So, blockchain genomics ventures are insufficient to establish the permissibility of genome collections and biobanks. I then discuss an additional ethical challenge to genomic biobanks. This challenge takes the form of doubting that future benefits that result from biomedical research dependent on genomic databases will outweigh the costs. Despite criticisms of genomic research, I claim that it is reasonable to think that future net benefits will be gained from research on such databases. Therefore, I conclude that there remains a dilemma in the ethics of genomic biobanks.
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Affiliation(s)
- Valérie Racine
- College of Arts & Sciences, Western New England University Springfield, Massachusetts, MA, USA.
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9
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Ducrée J, Etzrodt M, Bartling S, Walshe R, Harrington T, Wittek N, Posth S, Wittek K, Ionita A, Prinz W, Kogias D, Paixão T, Peterfi I, Lawton J. Unchaining Collective Intelligence for Science, Research, and Technology Development by Blockchain-Boosted Community Participation. FRONTIERS IN BLOCKCHAIN 2021. [DOI: 10.3389/fbloc.2021.631648] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Since its launch just over a decade ago by the cryptocurrency Bitcoin, the distributed ledger technology (DLT) blockchain has followed a breathtaking trajectory into manifold application spaces. This study aper analyses how key factors underpinning the success of this ground-breaking “Internet of value” technology, such as staking of collateral (“skin in the game”), competitive crowdsourcing, crowdfunding, and prediction markets, can be applied to substantially innovate the legacy organization of science, research, and technology development (RTD). Here, we elaborate a highly integrative, community-based strategy where a token-based crypto-economy supports finding best possible consensus, trust, and truth by adding unconventional elements known from reputation systems, betting, secondary markets, and social networking. These tokens support the holder’s formalized reputation and are used in liquid-democracy style governance and arbitration within projects or community-driven initiatives. This participatory research model serves as a solid basis for comprehensively leveraging collective intelligence by effectively incentivizing contributions from the crowd, such as intellectual property work, validation, assessment, infrastructure, education, assessment, governance, publication, and promotion of projects. On the analogy of its current blockbusters like peer-to-peer structured decentralized finance (“DeFi”), blockchain technology can seminally enhance the efficiency of science and RTD initiatives, even permitting to fully stage operations as a chiefless decentralized autonomous organization (DAOs).
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10
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Grishin D, Raisaro JL, Troncoso-Pastoriza JR, Obbad K, Quinn K, Misbach M, Gollhardt J, Sa J, Fellay J, Church GM, Hubaux JP. Citizen-centered, auditable and privacy-preserving population genomics. NATURE COMPUTATIONAL SCIENCE 2021; 1:192-198. [PMID: 38183193 DOI: 10.1038/s43588-021-00044-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 02/22/2021] [Indexed: 01/07/2024]
Abstract
The growing number of health-data breaches, the use of genomic databases for law enforcement purposes and the lack of transparency of personal genomics companies are raising unprecedented privacy concerns. To enable a secure exploration of genomic datasets with controlled and transparent data access, we propose a citizen-centric approach that combines cryptographic privacy-preserving technologies, such as homomorphic encryption and secure multi-party computation, with the auditability of blockchains. Our open-source implementation supports queries on the encrypted genomic data of hundreds of thousands of individuals, with minimal overhead. We show that real-world adoption of our system alleviates widespread privacy concerns and encourages data access sharing with researchers.
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Affiliation(s)
- Dennis Grishin
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
- Nebula Genomics Inc., San Francisco, CA, USA.
| | - Jean Louis Raisaro
- Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Data Science Group, Division of Information Systems, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Kamal Obbad
- Nebula Genomics Inc., San Francisco, CA, USA
| | - Kevin Quinn
- Nebula Genomics Inc., San Francisco, CA, USA
| | - Mickaël Misbach
- School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Joao Sa
- School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jacques Fellay
- Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - George M Church
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jean-Pierre Hubaux
- School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
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11
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Jones KM, Cook-Deegan R, Rotimi CN, Callier SL, Bentley AR, Stevens H, Phillips KA, Jansen JP, Weyant CF, Roberts DE, Zielinski D, Erlich Y, Garrison NA, Carroll SR, Ossorio PN, Moreau Y, Wang M. Complicated legacies: The human genome at 20. Science 2021; 371:564-569. [PMID: 33542123 PMCID: PMC8011351 DOI: 10.1126/science.abg5266] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Millions of people today have access to their personal genomic information. Direct-to-consumer services and integration with other “big data” increasingly commoditize what was rightly celebrated as a singular achievement in February 2001 when the first draft human genomes were published. But such remarkable technical and scientific progress has not been without its share of missteps and growing pains.
Science
invited the experts below to help explore how we got here and where we should (or ought not) be going.
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Affiliation(s)
- Kathryn Maxson Jones
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA
| | - Robert Cook-Deegan
- School for the Future of Innovation in Society and Consortium for Science, Policy and Outcomes, Arizona State University, Washington, DC, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD, USA
| | - Shawneequa L Callier
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD, USA
- Department of Clinical Research and Leadership, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD, USA
| | - Hallam Stevens
- School of Humanities, Nanyang Technological University, Singapore
| | - Kathryn A Phillips
- Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), Department of Clinical Pharmacy, University of California, San Francisco, CA, USA
- Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Jeroen P Jansen
- Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), Department of Clinical Pharmacy, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Christopher F Weyant
- Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), Department of Clinical Pharmacy, University of California, San Francisco, CA, USA
| | - Dorothy E Roberts
- Department of Africana Studies, Department of Sociology, and Law School, University of Pennsylvania, Philadelphia, PA, USA
| | - Dina Zielinski
- Université de Paris, INSERM 970, Paris Translational Research Centre for Organ Transplantation, Paris, France
- Doctoral School 515, Sorbonne Université, Paris, France
| | - Yaniv Erlich
- Efi Arazi School of Computer Science, IDC Herzliya, Herzliya, Israel
| | - Nanibaa' A Garrison
- Institute for Society & Genetics, University of California, Los Angeles, CA 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Division of General Internal Medicine & Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Stephanie Russo Carroll
- Native Nations Institute, Udall Center for Studies in Public Policy, University of Arizona, Tucson, AZ 85724, USA
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA
| | - Pilar N Ossorio
- Morgridge Institute for Research and University of Wisconsin Law School, Madison, WI, USA
| | - Yves Moreau
- University of Leuven (KU Leuven), Leuven, Belgium
| | - Maya Wang
- Human Rights Watch, New York, NY, USA
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12
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Almeida JR, Pratas D, Oliveira JL. A semi-automatic methodology for analysing distributed and private biobanks. Comput Biol Med 2020; 130:104180. [PMID: 33360272 DOI: 10.1016/j.compbiomed.2020.104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/14/2020] [Accepted: 12/14/2020] [Indexed: 10/22/2022]
Abstract
Privacy issues limit the analysis and cross-exploration of most distributed and private biobanks, often raised by the multiple dimensionality and sensitivity of the data associated with access restrictions and policies. These characteristics prevent collaboration between entities, constituting a barrier to emergent personalized and public health challenges, namely the discovery of new druggable targets, identification of disease-causing genetic variants, or the study of rare diseases. In this paper, we propose a semi-automatic methodology for the analysis of distributed and private biobanks. The strategies involved in the proposed methodology efficiently enable the creation and execution of unified genomic studies using distributed repositories, without compromising the information present in the datasets. We apply the methodology to a case study in the current Covid-19, ensuring the combination of the diagnostics from multiple entities while maintaining privacy through a completely identical procedure. Moreover, we show that the methodology follows a simple, intuitive, and practical scheme.
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Affiliation(s)
- João Rafael Almeida
- DETI/IEETA, University of Aveiro, Aveiro, Portugal; Department of Computation, University of A Coruña, A Coruña, Spain.
| | - Diogo Pratas
- DETI/IEETA, University of Aveiro, Aveiro, Portugal; Department of Virology, University of Helsinki, Helsinki, Finland.
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13
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14
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Leo S, Cherkaoui A, Renzi G, Schrenzel J. Mini Review: Clinical Routine Microbiology in the Era of Automation and Digital Health. Front Cell Infect Microbiol 2020; 10:582028. [PMID: 33330127 PMCID: PMC7734209 DOI: 10.3389/fcimb.2020.582028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 10/20/2020] [Indexed: 12/13/2022] Open
Abstract
Clinical microbiology laboratories are the first line to combat and handle infectious diseases and antibiotic resistance, including newly emerging ones. Although most clinical laboratories still rely on conventional methods, a cascade of technological changes, driven by digital imaging and high-throughput sequencing, will revolutionize the management of clinical diagnostics for direct detection of bacteria and swift antimicrobial susceptibility testing. Importantly, such technological advancements occur in the golden age of machine learning where computers are no longer acting passively in data mining, but once trained, can also help physicians in making decisions for diagnostics and optimal treatment administration. The further potential of physically integrating new technologies in an automation chain, combined to machine-learning-based software for data analyses, is seducing and would indeed lead to a faster management in infectious diseases. However, if, from one side, technological advancement would achieve a better performance than conventional methods, on the other side, this evolution challenges clinicians in terms of data interpretation and impacts the entire hospital personnel organization and management. In this mini review, we discuss such technological achievements offering practical examples of their operability but also their limitations and potential issues that their implementation could rise in clinical microbiology laboratories.
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Affiliation(s)
- Stefano Leo
- Genomic Research Laboratory, Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Abdessalam Cherkaoui
- Bacteriology Laboratory, Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
| | - Gesuele Renzi
- Bacteriology Laboratory, Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
| | - Jacques Schrenzel
- Genomic Research Laboratory, Division of Infectious Diseases, Department of Medicine, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- Bacteriology Laboratory, Division of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals, Geneva, Switzerland
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15
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Privacy-Preserving Distributed Analytics in Fog-Enabled IoT Systems. SENSORS 2020; 20:s20216153. [PMID: 33138072 PMCID: PMC7662678 DOI: 10.3390/s20216153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/13/2020] [Accepted: 10/26/2020] [Indexed: 12/02/2022]
Abstract
The Internet of Things (IoT) has evolved significantly with advances in gathering data that can be extracted to provide knowledge and facilitate decision-making processes. Currently, IoT data analytics encountered challenges such as growing data volumes collected by IoT devices and fast response requirements for time-sensitive applications in which traditional Cloud-based solution is unable to meet due to bandwidth and high latency limitations. In this paper, we develop a distributed analytics framework for fog-enabled IoT systems aiming to avoid raw data movement and reduce latency. The distributed framework leverages the computational capacities of all the participants such as edge devices and fog nodes and allows them to obtain the global optimal solution locally. To further enhance the privacy of data holders in the system, a privacy-preserving protocol is proposed using cryptographic schemes. Security analysis was conducted and it verified that exact private information about any edge device’s raw data would not be inferred by an honest-but-curious neighbor in the proposed secure protocol. In addition, the accuracy of solution is unaffected in the secure protocol comparing to the proposed distributed algorithm without encryption. We further conducted experiments on three case studies: seismic imaging, diabetes progression prediction, and Enron email classification. On seismic imaging problem, the proposed algorithm can be up to one order of magnitude faster than the benchmarks in reaching the optimal solution. The evaluation results validate the effectiveness of the proposed methodology and demonstrate its potential to be a promising solution for data analytics in fog-enabled IoT systems.
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16
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Strategic vision for improving human health at The Forefront of Genomics. Nature 2020; 586:683-692. [PMID: 33116284 DOI: 10.1038/s41586-020-2817-4] [Citation(s) in RCA: 168] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 09/04/2020] [Indexed: 12/20/2022]
Abstract
Starting with the launch of the Human Genome Project three decades ago, and continuing after its completion in 2003, genomics has progressively come to have a central and catalytic role in basic and translational research. In addition, studies increasingly demonstrate how genomic information can be effectively used in clinical care. In the future, the anticipated advances in technology development, biological insights, and clinical applications (among others) will lead to more widespread integration of genomics into almost all areas of biomedical research, the adoption of genomics into mainstream medical and public-health practices, and an increasing relevance of genomics for everyday life. On behalf of the research community, the National Human Genome Research Institute recently completed a multi-year process of strategic engagement to identify future research priorities and opportunities in human genomics, with an emphasis on health applications. Here we describe the highest-priority elements envisioned for the cutting-edge of human genomics going forward-that is, at 'The Forefront of Genomics'.
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17
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Kumar R, Dhanda SK. Current Status on Population Genome Catalogues in different Countries. Bioinformation 2020; 16:297-300. [PMID: 32773988 PMCID: PMC7392090 DOI: 10.6026/97320630016297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 04/01/2020] [Indexed: 11/23/2022] Open
Abstract
Genomics has become indispensable for research in the last two decades. Completed and ongoing genome projects such as the UK Biobank, ICGC, TCGA and GenomeAsia100K have helped to understand several life-threatening diseases like cancer. Such initiatives from different countries have offered genomics-based diagnostics along with glues for therapies towards personalized healthcare. The Indian Agencies has started initiatives to catalogue the genome sequences of 20,000 individuals. The Department of Biotechnology (DBT) along with other scientific agencies has plans to sequence 10,000 healthy individuals and 10,000 diseased individuals. The Council of Scientific and Industrial Research (CSIR) also developed the "IndiGen" genome project where genome sequences for 1008 individuals are made available in Phase I. This will enable the development of a genome catalogue to introduce novel genomics-based clinical applications in future healthcare plan.
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Affiliation(s)
- Rahul Kumar
- Institute for Cancer Genetics, Columbia University Irving Medical Center, New York, NY10032, USA
| | - Sandeep Kumar Dhanda
- Department of Oncology, Saint Jude Children's Research Hospital, Memphis, TN 38105, USA
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18
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Arends MJ, Salto-Tellez M. Low-contact and high-interconnectivity pathology (LC&HI Path): post-COVID19-pandemic practice of pathology. Histopathology 2020; 77:518-524. [PMID: 32516836 PMCID: PMC7300838 DOI: 10.1111/his.14174] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The COVID-19 pandemic situation may be viewed as an opportunity to accelerate some of the ongoing transformations in modern pathology. This refers primarily to the digitalisation of the practice of tissue and cellular pathology diagnostics. However, it is also an opportunity to analyse the modus operandi of a discipline that has been practised in a similar manner for more than 100 years. The challenge is to define the next generation of interconnectivity tools that would be necessary to achieve a new operational model that, while ensuring low face-to-face interaction between the main players of the diagnostic pipeline, allows maximum interconnectivity to serve our patients and the immediate teaching and research needs associated with clinical tissue/cellular samples. This viewpoint aims to describe what this new paradigm, a low-contact and high-interconnectivity pathology (LC&HC Path) operation, may require in the near future.
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Affiliation(s)
- Mark J Arends
- Division of Cancer, University of Edinburgh, Cancer Research UK Edinburgh Centre, IGMM, Western General Hospital Campus, Edinburgh, UK
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK.,Cellular Pathology, Belfast Health and Social Care Trust, Belfast, UK
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19
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Kaissis GA, Makowski MR, Rückert D, Braren RF. Secure, privacy-preserving and federated machine learning in medical imaging. NAT MACH INTELL 2020. [DOI: 10.1038/s42256-020-0186-1] [Citation(s) in RCA: 216] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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20
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Dankar FK, Gergely M, Malin B, Badji R, Dankar SK, Shuaib K. Dynamic-informed consent: A potential solution for ethical dilemmas in population sequencing initiatives. Comput Struct Biotechnol J 2020; 18:913-921. [PMID: 32346464 PMCID: PMC7182686 DOI: 10.1016/j.csbj.2020.03.027] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 03/29/2020] [Accepted: 03/30/2020] [Indexed: 01/13/2023] Open
Abstract
While the majority of population-level genome sequencing initiatives claim to follow the principles of informed consent, the requirements for informed consent have not been-well defined in this context. In fact, the implementation of informed consent differs greatly across these initiatives - spanning broad consent, blanket consent, and tiered consent among others. As such, this calls for an investigation into the requirements for consent to be "informed" in the context of population genomics. One particular strategy that claims to be fully informed and to continuously engage participants is called "dynamic consent". Dynamic consent is based on a personalised communication platform that aims to facilitate the consent process. It is oriented to support continuous two-way communication between researchers and participants. In this paper, we analyze the requirements of informed consent in the context of population genomics, review various current implementations of dynamic consent, assess whether they fulfill the requirement of informed consent, and, in turn, enable participants to make autonomous and informed choices on whether or not to participate in research projects.
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Affiliation(s)
- Fida K. Dankar
- College of Information Technology, UAEU, Al-Ain, United Arab Emirates
| | - Marton Gergely
- College of Information Technology, UAEU, Al-Ain, United Arab Emirates
| | - Bradley Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | | | | | - Khaled Shuaib
- College of Information Technology, UAEU, Al-Ain, United Arab Emirates
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