scholarly journals Accelerating Genomic Data Generation and Facilitating Genomic Data Access Using Decentralization, Privacy-Preserving Technologies and Equitable Compensation

2018 ◽  
Vol 1 ◽  
pp. 1-23 ◽  
Author(s):  
Dennis Grishin ◽  
Kamal Obbad ◽  
Preston Estep ◽  
Kevin Quinn ◽  
Sarah Wait Zaranek ◽  
...  
Keyword(s):  
Genomic Data ◽  
Data Access ◽  
Privacy Preserving ◽  
Data Generation

scholarly journals Citizen-Centered, Auditable, and Privacy-Preserving Population Genomics

2019 ◽  
Author(s):  
Dennis Grishin ◽  
Jean Louis Raisaro ◽  
Juan Ramón Troncoso-Pastoriza ◽  
Kamal Obbad ◽  
Kevin Quinn ◽  
...  
Keyword(s):  
Open Source ◽  
Population Genomics ◽  
Homomorphic Encryption ◽  
Genomic Data ◽  
Data Access ◽  
Privacy Preserving ◽  
Genomic Databases ◽  
Privacy Concerns ◽  
Novel Approach ◽  
Security Guarantees

AbstractThe 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 novel approach that combines cryptographic privacy-preserving technologies, such as homomorphic encryption and secure multi-party computation, with the auditability of blockchains. This approach provides strong security guarantees against realistic threat models by empowering individual citizens to decide who can query and access their genomic data and by ensuring end-to-end data confidentiality. Our open-source implementation supports queries on the encrypted genomic data of hundreds of thousands of individuals, with minimal overhead. Our work opens a path towards multi-functional, privacy-preserving genomic-data analysis.One Sentence SummaryA citizen-centered open-source response to the privacy concerns that hinder population genomics, based on modern cryptography.

scholarly journals Privacy preserving cloud data access with multi-authorities

2013 ◽  
Author(s):  
Taeho Jung ◽  
Xiang-Yang Li ◽  
Zhiguo Wan ◽  
Meng Wan
Keyword(s):  
Data Access ◽  
Privacy Preserving ◽  
Cloud Data

scholarly journals Privacy-preserving techniques of genomic data—a survey

2017 ◽  
Vol 20 (3) ◽  
pp. 887-895 ◽  
Author(s):  
Md Momin Al Aziz ◽  
Md Nazmus Sadat ◽  
Dima Alhadidi ◽  
Shuang Wang ◽  
Xiaoqian Jiang ◽  
...  
Keyword(s):  
Genomic Data ◽  
Privacy Preserving

scholarly journals Study of privacy-preserving framework for cloud storage

2011 ◽  
Vol 8 (3) ◽  
pp. 801-819 ◽  
Author(s):  
Huang Ruwei ◽  
Gui Xiaolin ◽  
Yu Si ◽  
Zhuang Wei
Keyword(s):  
Data Storage ◽  
Cloud Storage ◽  
Security Analysis ◽  
Bloom Filter ◽  
Data Access ◽  
Data Retrieval ◽  
Privacy Preserving ◽  
Index Structure ◽  
Structure Generation ◽  
Access Right

In order to implement privacy-preserving, efficient and secure data storage and access environment of cloud storage, the following problems must be considered: data index structure, generation and management of keys, data retrieval, treatments of change of users? access right and dynamic operations on data, and interactions among participants. To solve those problems, the interactive protocol among participants is introduced, an extirpation-based key derivation algorithm (EKDA) is designed to manage the keys, a double hashed and weighted Bloom Filter (DWBF) is proposed to retrieve the encrypted keywords, which are combined with lazy revocation, multi-tree structure, asymmetric and symmetric encryptions, which form a privacypreserving, efficient and secure framework for cloud storage. The experiment and security analysis show that EKDA can reduce the communication and storage overheads efficiently, DWBF supports ciphertext retrieval and can reduce communication, storage and computation overhead as well, and the proposed framework is privacy preserving while supporting data access efficiently.

scholarly journals Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

2019 ◽  
Author(s):  
Kate Chkhaidze ◽  
Timon Heide ◽  
Benjamin Werner ◽  
Marc J. Williams ◽  
Weini Huang ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Tumour Growth ◽  
Evolutionary Dynamics ◽  
Clonal Selection ◽  
Genomic Data ◽  
Confounding Factors ◽  
Data Generation ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

AbstractQuantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constrains, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data.SummarySequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected, impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mathematical models and computer simulations that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial simulation of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to overcome these biases and more robustly measure aspects of the biology of tumours from the data.

scholarly journals Genomic Data-Sharing Practices

2019 ◽  
Vol 47 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Angela G. Villanueva ◽  
Robert Cook-Deegan ◽  
Jill O. Robinson ◽  
Amy L. McGuire ◽  
Mary A. Majumder
Keyword(s):  
Data Sharing ◽  
Medical Information ◽  
Public Information ◽  
Genomic Data ◽  
Data Access ◽  
Privacy And Security ◽  
Participant Engagement ◽  
Information Commons ◽  
Derived Data

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.

Game theory for privacy-preserving sharing of genomic data

2020 ◽  
pp. 135-160
Author(s):  
Zhiyu Wan ◽  
Yevgeniy Vorobeychik ◽  
Ellen Wright Clayton ◽  
Murat Kantarcioglu ◽  
Bradley Malin
Keyword(s):  
Game Theory ◽  
Genomic Data ◽  
Privacy Preserving
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