A community effort to protect genomic data sharing, collaboration and outsourcing

Abstract

The human genome can reveal sensitive information and is potentially re-identifiable, which raises privacy and security concerns about sharing such data on wide scales. In 2016, we organized the third Critical Assessment of Data Privacy and Protection competition as a community effort to bring together biomedical informaticists, computer privacy and security researchers, and scholars in ethical, legal, and social implications (ELSI) to assess the latest advances on privacy-preserving techniques for protecting human genomic data. Teams were asked to develop novel protection methods for emerging genome privacy challenges in three scenarios: Track (1) data sharing through the Beacon service of the Global Alliance for Genomics and Health. Track (2) collaborative discovery of similar genomes between two institutions; and Track (3) data outsourcing to public cloud services. The latter two tracks represent continuing themes from our 2015 competition, while the former was new and a response to a recently established vulnerability. The winning strategy for Track 1 mitigated the privacy risk by hiding approximately 11% of the variation in the database while permitting around 160,000 queries, a significant improvement over the baseline. The winning strategies in Tracks 2 and 3 showed significant progress over the previous competition by achieving multiple orders of magnitude performance improvement in terms of computational runtime and memory requirements. The outcomes suggest that applying highly optimized privacy-preserving and secure computation techniques to safeguard genomic data sharing and analysis is useful. However, the results also indicate that further efforts are needed to refine these techniques into practical solutions.

Secure Outsourced Matrix Computation and Application to Neural Networks

“…This property makes it very attractive for secure outsourcing tasks, including financial model evaluation and genetic testing, which can ensure the privacy and security of data communication, storage, and computation [3, 46]. In biomedicine, it is extremely attractive due to the privacy concerns about patients’ sensitive data [28, 47]. Recently deep neural network based models have been demonstrated to achieve great success in a number of health care applications [36], and a natural question is whether we can outsource such learned models to a third party and evaluate new samples in a secure manner?…”

DNA Data Marketplace: An Analysis of the Ethical Concerns Regarding the Participation of the Individuals

“…Sharing personal genomic data raises considerable privacy and security concerns, due to unique nature of genomic data that contains identifiers which makes the complete de-identification of the data hard if not impossible (Wang et al, 2017). In addition, genomic data can reveal a wide range of sensitive health and non-health related data about the individuals and their family members (Genomeweb, 2018).…”

Genomic Common Data Model for Seamless Interoperation of Biomedical Data in Clinical Practice: Retrospective Study

“…Patients’ sequencing data are currently generated through relatively large-scale projects aimed at exploring the role of clinical NGS in precision medicine conducted by organizations such as the American Association for Cancer Research Project GENIE [4] and the China Precision Medicine Initiative [5]. However, genomic data are considered to be privacy sensitive and potentially reidentifiable, which raises concerns about transmitting and sharing patient-level data outside of host institutions for collaborative research [6]. In addition, genomic sequencing data of subjects in a predefined cohort cannot reflect the full diversity of the entire population at the point of care, which limits the practical application of the data for research purposes [7].…”