Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
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
Active clinical and translational researchers across genetics, genomics, molecular biology, molecular diagnostics, and bioinformatics; pharmacologists; genetic counselors. Students at the graduate level and above in genetics, genomics, bioinformatics, molecular biology, and pharmaceutical science
Section I: Privacy Challenges in Genomic Data Sharing
1. Criticality of Data Sharing in Genomic Research & Public views of genomic data sharing
2. Genomic data access policy models
3. Information leaks in aggregate genomic data, inference attacks
4. Genealogical search using whole genome genotype profiles
Yuan Wei, Ryan Lewis, Ardalan Naseri, Shaojie Zhang, Degui Zhi
Section II: Privacy-Preserving Techniques for Responsible Genomic Data Sharing
5. Homomorphic encryption
6. Secure Multiparty Computing (MPC)
7. Game-Theoretic Approaches (for Genomic Privacy)
8. Hardware (SGX)
Somnath Chakrabarti, Thomas Knauth, Dmitrii Kuvaiskii, Michael Steiner and Mona Vij
- No. of pages:
- © Academic Press 2020
- 14th March 2020
- Academic Press
- Paperback ISBN:
- eBook ISBN:
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.
Associate Professor, Carnegie Mellon University, School of Computer Science
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.
Professor, Department of Computer Science, Indiana University, Bloomington, IN, USA
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.