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Guo C, Liu Y, Na M, Song J. Dual-Layer Index for Efficient Traceability Query of Food Supply Chain Based on Blockchain. Foods 2023; 12:foods12112267. [PMID: 37297511 DOI: 10.3390/foods12112267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/25/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023] Open
Abstract
Blockchain techniques have been introduced to achieve decentralized and transparent traceability systems, which are critical components of food supply chains. Academia and industry have tried to enhance the efficiency of blockchain-based food supply chain traceability queries. However, the cost of traceability queries remains high. In this paper, we propose a dual-layer index structure for optimizing traceability queries in blockchain, which consists of an external and an internal index. The dual-layer index structure accelerates the external block jump and internal transaction search while preserving the original characteristics of the blockchain. We establish an experimental environment by modeling the blockchain storage module for extensive simulation experiments. The results show that although the dual-layer index structure introduces a little extra storage and construction time, it significantly improves the efficiency of traceability queries. Specifically, the dual-layer index improves the traceability query rate by seven to eight times compared with that of the original blockchain.
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Affiliation(s)
- Chaopeng Guo
- Software College, Northeastern University, Shenyang 110000, China
| | - Yiming Liu
- Software College, Northeastern University, Shenyang 110000, China
| | - Meiyu Na
- Software College, Northeastern University, Shenyang 110000, China
| | - Jie Song
- Software College, Northeastern University, Shenyang 110000, China
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2
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Abstract
Genomics data are important for advancing biomedical research, improving clinical care, and informing other disciplines such as forensics and genealogy. However, privacy concerns arise when genomic data are shared. In particular, the identifying nature of genetic information, its direct relationship to health status, and the potential financial harm and stigmatization posed to individuals and their blood relatives call for a survey of the privacy issues related to sharing genetic and related data and potential solutions to overcome these issues. In this work, we provide an overview of the importance of genomic privacy, the information gleaned from genomics data, the sources of potential private information leakages in genomics, and ways to preserve privacy while utilizing the genetic information in research. We discuss the relationship between trust in the scientific community and protecting privacy, illuminating a future roadmap for data sharing and study participation.
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Affiliation(s)
- Gamze Gürsoy
- Department of Biomedical Informatics, Columbia University, New York, NY, USA; .,New York Genome Center, New York, NY, USA
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3
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Functional genomics data: privacy risk assessment and technological mitigation. Nat Rev Genet 2022; 23:245-258. [PMID: 34759381 DOI: 10.1038/s41576-021-00428-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2021] [Indexed: 12/15/2022]
Abstract
The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.
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Beyene M, Toussaint PA, Thiebes S, Schlesner M, Brors B, Sunyaev A. OUP accepted manuscript. J Am Med Inform Assoc 2022; 29:1433-1444. [PMID: 35595301 PMCID: PMC9277639 DOI: 10.1093/jamia/ocac077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/28/2022] [Accepted: 05/05/2022] [Indexed: 11/18/2022] Open
Abstract
Objective Rising interests in distributed ledger technology (DLT) and genomics have sparked various interdisciplinary research streams with a proliferating number of scattered publications investigating the application of DLT in genomics. This review aims to uncover the current state of research on DLT in genomics, in terms of focal research themes and directions for future research. Materials and Methods We conducted a scoping review and thematic analysis. To identify the 60 relevant papers, we queried Scopus, Web of Science, PubMed, ACM Digital Library, IEEE Xplore, arXiv, and BiorXiv. Results Our analysis resulted in 7 focal themes on DLT in genomics discussed in literature, namely: (1) Data economy and sharing; (2) Data management; (3) Data protection; (4) Data storage; (5) Decentralized data analysis; (6) Proof of useful work; and (7) Ethical, legal, and social implications. Discussion Based on the identified themes, we present 7 future research directions: (1) Investigate opportunities for the application of DLT concepts other than Blockchain; (2) Explore people’s attitudes and behaviors regarding the commodification of genetic data through DLT-based genetic data markets; (3) Examine opportunities for joint consent management via DLT; (4) Investigate and evaluate data storage models appropriate for DLT; (5) Research the regulation-compliant use of DLT in healthcare information systems; (6) Investigate alternative consensus mechanisms based on Proof of Useful Work; and (7) Explore DLT-enabled approaches for the protection of genetic data ensuring user privacy. Conclusion While research on DLT in genomics is currently growing, there are many unresolved problems. This literature review outlines extant research and provides future directions for researchers and practitioners.
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Affiliation(s)
- Mikael Beyene
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
- HIDSS4Health—Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - Philipp A Toussaint
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
- HIDSS4Health—Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - Scott Thiebes
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Matthias Schlesner
- Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
- Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Translational Oncology, National Center for Tumor Diseases, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ali Sunyaev
- Corresponding Author: Ali Sunyaev, Department of Economics and Management, Karlsruhe Institute of Technology, Kaiserstr. 89, 76133 Karlsruhe, Germany;
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Lu D, Zhang Y, Zhang L, Wang H, Weng W, Li L, Cai H. Methods of privacy-preserving genomic sequencing data alignments. Brief Bioinform 2021; 22:6279828. [PMID: 34021302 DOI: 10.1093/bib/bbab151] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/10/2021] [Accepted: 03/30/2021] [Indexed: 11/14/2022] Open
Abstract
Genomic data alignment, a fundamental operation in sequencing, can be utilized to map reads into a reference sequence, query on a genomic database and perform genetic tests. However, with the reduction of sequencing cost and the accumulation of genome data, privacy-preserving genomic sequencing data alignment is becoming unprecedentedly important. In this paper, we present a comprehensive review of secure genomic data comparison schemes. We discuss the privacy threats, including adversaries and privacy attacks. The attacks can be categorized into inference, membership, identity tracing and completion attacks and have been applied to obtaining the genomic privacy information. We classify the state-of-the-art genomic privacy-preserving alignment methods into three different scenarios: large-scale reads mapping, encrypted genomic datasets querying and genetic testing to ease privacy threats. A comprehensive analysis of these approaches has been carried out to evaluate the computation and communication complexity as well as the privacy requirements. The survey provides the researchers with the current trends and the insights on the significance and challenges of privacy issues in genomic data alignment.
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Affiliation(s)
- Dandan Lu
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Yue Zhang
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510006, China
| | - Ling Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, P. R. China,510060
| | - Haiyan Wang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Wanlin Weng
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Li Li
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Hongmin Cai
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
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Arshad S, Arshad J, Khan MM, Parkinson S. Analysis of security and privacy challenges for DNA-genomics applications and databases. J Biomed Inform 2021; 119:103815. [PMID: 34022422 DOI: 10.1016/j.jbi.2021.103815] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/07/2021] [Accepted: 05/08/2021] [Indexed: 02/06/2023]
Abstract
DNA technology is rapidly moving towards digitization. Scientists use software tools and applications for sequencing, synthesizing, analyzing and sharing of DNA and genomic data, operate lab equipment and store genetic information in shared datastores. Using cutting-edge computing methods and techniques, researchers have decoded human genome, created organisms with new capabilities, automated drug development and transformed food safety. Such software applications are typically developed to progress scientific understanding and as such cyber security is never a concern for these applications. However, with the increasing commercialisation of DNA technologies, coupled with the sensitivity of DNA data, there is a need to adopt a security-by-design approach. In this paper we investigate bio-cyber security threats to genomic-DNA data and software applications making use of such data to advance scientific research. Specifically, we adopt an empirical approach to analyse and identify vulnerabilities within genomic-DNA databases and bioinformatics software applications that can lead to cyber-attacks affecting the confidentiality, integrity and availability of such sensitive data. We present a detailed analysis of these threats and highlight potential protection mechanisms to help researchers pursue these research directions.
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Affiliation(s)
- Saadia Arshad
- Department of Computer Science & IT, NED University of Engineering and Technology, Karachi, Pakistan
| | - Junaid Arshad
- School of Computing and Digital Technology, Birmingham City University, Birmingham, UK.
| | - Muhammad Mubashir Khan
- Department of Computer Science & IT, NED University of Engineering and Technology, Karachi, Pakistan
| | - Simon Parkinson
- Department of Computer Science, University of Huddersfield, Huddersfield, UK
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Kuo TT, Jiang X, Tang H, Wang X, Bath T, Bu D, Wang L, Harmanci A, Zhang S, Zhi D, Sofia HJ, Ohno-Machado L. iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching. BMC Med Genomics 2020; 13:98. [PMID: 32693816 PMCID: PMC7372776 DOI: 10.1186/s12920-020-0715-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Tsung-Ting Kuo
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Xiaoqian Jiang
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Haixu Tang
- School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - XiaoFeng Wang
- School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Tyler Bath
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Diyue Bu
- School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Lei Wang
- School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Arif Harmanci
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Shaojie Zhang
- Department of Computer Science, University of Southern Florida, Orlando, FL, 32816, USA
| | - Degui Zhi
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Heidi J Sofia
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lucila Ohno-Machado
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA.
- Division of Health Services Research & Development, VA San Diego Healthcare System, San Diego, CA, 92161, USA.
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