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E-raamat: Responsible Genomic Data Sharing: Challenges and Approaches

Edited by (Associate Professor, Carnegie Mellon University, School of Computer Science), Edited by (Professor, Department of Computer Science, Indiana University, Bloomington, IN, USA)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 14-Mar-2020
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128163399
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Responsible Genomic Data Sharing: Challenges and ApproachesSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 14-Mar-2020
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128163399
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Responsible Genomic Data Sharing: Challenges and Approaches brings together international experts in genomics research, bioinformatics and digital security who analyze common challenges in genomic data sharing, privacy preserving technologies, and best practices for large-scale genomic data sharing. Practical case studies, including the Global Alliance for Genomics and Health, the Beacon Network, and the Matchmaker Exchange, are discussed in-depth, illuminating pathways forward for new genomic data sharing efforts across research and clinical practice, industry and academia.

  • Addresses privacy preserving technologies and how they can be applied to enable responsible genomic data sharing
  • Employs illustrative case studies and analyzes emerging genomic data sharing efforts, common challenges and lessons learned
  • Features chapter contributions from international experts in responsible approaches to genomic data sharing
Contributors xi
SECTION I Privacy challenges in genomic data sharing
Chapter 1 Criticality of data sharing in genomic research and public views of genomic data sharing
3(16)
Gamze Gursoy
1 Introduction
3(1)
2 Advancing research and scientific knowledge
4(1)
3 Importance of genomic data sharing in curing diseases
5(6)
3.1 Rare disease perspective
5(1)
3.2 Cancer perspective
6(3)
3.3 Genome-wide association studies perspective
9(2)
4 Impact of large-scale data sharing on basic research and discovery
11(4)
4.1 The International HapMap Project
11(1)
4.2 1000 Genomes Project
12(1)
4.3 The Cancer Genome Atlas project
13(1)
4.4 Encyclopedia of DNA Elements Project
13(1)
4.5 Genotype-Tissue Expression Project
14(1)
5 Reproducibility
15(1)
6 Patient/public perspective
15(1)
Acknowledgments
16(1)
References
16(3)
Chapter 2 Genomic data access policy models
19(14)
Stephanie O.M. Dyke
1 Data-sharing policy developments
19(1)
2 Open-access policy model
20(2)
3 Controlled-access policy model
22(3)
4 Registered-access policy model
25(3)
5 Ongoing concerns and developments
28(2)
5.1 Maintaining consent: Consent Codes
28(1)
5.2 Data-sharing risk assessment: choosing the right access level
29(1)
References
30(3)
Chapter 3 Information leaks in genomic data: inference attacks
33(18)
Erman Ayday
1 Inference attacks on statistical genomic databases
33(1)
2 Inference attacks on genomic data-sharing beacons
34(4)
3 Inference attacks on kin genomic privacy
38(5)
4 Inference attacks using genotype---phenotype associations
43(3)
5 Conclusions
46(1)
Acknowledgments
47(1)
References
47(4)
Chapter 4 Genealogical search using whole-genome genotype profiles
51(46)
Yuan Wei
Ryan Lewis
Ardalan Naseri
Shaojie Zhang
Degui Zhi
1 Introduction
51(1)
2 History of personal genetic data
51(2)
2.1 HapMap
52(1)
2.2 1000 genomes Project
52(1)
2.3 UK Biobank and beyond
52(1)
3 Direct-to-consumer genetic companies
53(3)
3.1 Early days
53(1)
3.2 Growth
54(1)
3.3 Current trends
54(1)
3.4 GEDMatch and others
55(1)
4 How to encode genotype information at the genome scale
56(12)
4.1 Genotype
56(2)
4.2 Haplotype
58(1)
4.3 Phasing
58(2)
4.4 File format
60(8)
5 Identity-by-descent segment and familial relatedness
68(8)
5.1 Genetic distance
68(2)
5.2 What is IBD
70(1)
5.3 Cousin nomenclature
71(1)
5.4 How IBD is related to family relationships
72(2)
5.5 Expected IBD family sharing
74(2)
6 Genealogical search
76(5)
6.1 What is a genealogy search
76(2)
6.2 Genotype-based method
78(1)
6.3 Haplotype-based method
79(2)
6.4 Benchmarking of IBD detection: runtime, power, and accuracy
81(1)
7 Practical methods
81(1)
7.1 Methods used by DTC companies
81(1)
8 Challenges and unmet needs
82(2)
8.1 Ancestry bias
82(2)
8.2 Phasing imperfection
84(1)
8.3 Benchmarking of genealogical search
84(1)
9 Privacy concerns
84(2)
10 Conclusions
86(1)
Acknowledgments
86(1)
References
86(11)
SECTION II Privacy-preserving techniques for responsible genomic data sharing
Chapter 5 Homomorphic encryption
97(26)
Kim Laine
1 Overview
97(4)
1.1 Early ideas
98(1)
1.2 Homomorphic encryption
98(1)
1.3 Note about terminology
99(1)
1.4 Implementations
99(1)
1.5 Standardization
100(1)
1.6 Applications
100(1)
2 Homomorphic encryption
101(15)
2.1 What is encryption?
101(1)
2.2 Partially homomorphic encryption
101(1)
2.3 Mathematical background
102(2)
2.4 (Ring) Learning With Errors
104(2)
2.5 The Brakerski---Fan---Vercauteren scheme
106(6)
2.6 Computing on encrypted integers
112(1)
2.7 Batching
112(2)
2.8 Approximate arithmetic on encrypted numbers
114(1)
2.9 Other schemes
115(1)
3 Applications
116(2)
3.1 Outsourced storage and computation
116(1)
3.2 Private prediction
116(1)
3.3 Private learning
117(1)
3.4 PSI and PIR
117(1)
3.5 Biomedical applications
118(1)
4 Future Outlook
118(2)
4.1 Complexity
118(1)
4.2 Usability
119(1)
4.3 Hardware
119(1)
5 Conclusions
120(1)
References
120(3)
Chapter 6 Secure multi-party computation
123(12)
Yan Huang
1 A brief overview
123(1)
2 Defining security
124(2)
3 Protocols
126(7)
3.1 Oblivious transfer
127(1)
3.2 Multiplicative triples
127(1)
3.3 Generic MPC in linear rounds
127(1)
3.4 Generic MPC in constant rounds
128(5)
3.5 Pool-based cut-and-choose
133(1)
References
133(2)
Chapter 7 Game theory for privacy-preserving sharing of genomic data
135(26)
Zhiyu Wan
Yevgeniy Vorobeychik
Ellen Wright Clayton
Murat Kantarcioglu
Bradley Malin
1 Introduction
135(2)
2 Background
137(1)
3 Membership-inference game
138(4)
3.1 The game and its solutions
138(3)
3.2 Limitations of the model
141(1)
4 Beacon service game
142(8)
4.1 The game and its solutions
142(7)
4.2 Limitations of the model
149(1)
5 Kinship game
150(3)
5.1 The game and its solutions
150(2)
5.2 Limitations of the model
152(1)
6 Re-identification game
153(2)
6.1 The game and its solutions
153(2)
6.2 Limitations of the model
155(1)
7 Discussion
155(2)
8 Conclusions
157(1)
Acknowledgments
157(1)
References
157(4)
Chapter 8 Trusted execution environment with Intel SGX
161(30)
Somnath Chakrabarti
Thomas Knauth
Dmitrii Kuvaiskii
Michael Steiner
Mona Vij
1 Introduction
161(2)
2 Trusted execution environment
163(3)
3 Intel Software Guard Extensions
166(16)
3.1 Hardware architecture
168(7)
3.2 Software development
175(3)
3.3 Security properties
178(3)
3.4 Performance properties
181(1)
4 HW-MPC and SGX in cloud
182(6)
4.1 TEEs in the cloud
183(1)
4.2 Developing with SGX in the cloud
183(1)
4.3 Deploying SGX applications
184(2)
4.4 Open questions on SGX in the cloud
186(1)
4.5 Putting it all together
187(1)
5 Outlook
188(1)
References
189(2)
Index 191
Dr. Jiang is a Christopher Sarofim associate professor and center director for health security and phenotyping in the School of Biomedical Informatics (SBMI) at The University of Texas Health Science Center at Houston (UTHealth). Before joining UThealth, he was an associate professor with tenure in the Department of Biomedical Informatics (DBMI) at UCSD. He is an associate editor of BMC Medical Informatics and Decision Making and served as the editorial board member of Journal of American Medical Informatics Association. He works primarily in health data privacy and predictive models in biomedicine. He received CPRIT Rising Stars and UT Stars awards and best and distinguished paper awards from American Medical Informatics Association (AMIA) Joint Summits on Translational Science (2012, 2013, 2016). He is one of the organizers of the iDASH Genome Privacy Workshops, which was reported by Nature News and GenomeWeb. Dr. Haixu Tang is a Professor of Computer Science and the Director of Data Science Academic Programs in School of Informatics, Computing, and Engineering at Indiana University, Bloomington. His primary research interests include algorithmic statistical problems in genomics and proteomics, and has been working on genome privacy protection algorithms since 2008. He received the NSF CAREER Award in 2007, and the PETS award for his work in genome privacy in 2009. He is one of the organizers of the iDASH Genome Privacy Workshops.
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