Privacy-Preserving Genetic Relatedness Test

An increasing number of individuals are turning to Direct-To-Consumer (DTC) genetic testing to learn about their predisposition to diseases, traits, and/or ancestry. DTC companies like 23andme and Ancestry.com have started to offer popular and affordable ancestry and genealogy tests, with services allowing users to find unknown relatives and long-distant cousins. Naturally, access and possible dissemination of genetic data prompts serious privacy concerns, thus motivating the need to design efficient primitives supporting private genetic tests. In this paper, we present an effective protocol for privacy-preserving genetic relatedness test (PPGRT), enabling a cloud server to run relatedness tests on input an encrypted genetic database and a test facility's encrypted genetic sample. We reduce the test to a data matching problem and perform it, privately, using searchable encryption. Finally, a performance evaluation of hamming distance based PP-GRT attests to the practicality of our proposals.Comment: A preliminary version of this paper appears in the Proceedings of the 3rd International Workshop on Genome Privacy and Security (GenoPri'16

Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective

Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies (PoPETs), Vol. 2019, Issue

Privacy preserving protocol for detecting genetic relatives using rare variants.

MotivationHigh-throughput sequencing technologies have impacted many areas of genetic research. One such area is the identification of relatives from genetic data. The standard approach for the identification of genetic relatives collects the genomic data of all individuals and stores it in a database. Then, each pair of individuals is compared to detect the set of genetic relatives, and the matched individuals are informed. The main drawback of this approach is the requirement of sharing your genetic data with a trusted third party to perform the relatedness test.ResultsIn this work, we propose a secure protocol to detect the genetic relatives from sequencing data while not exposing any information about their genomes. We assume that individuals have access to their genome sequences but do not want to share their genomes with anyone else. Unlike previous approaches, our approach uses both common and rare variants which provide the ability to detect much more distant relationships securely. We use a simulated data generated from the 1000 genomes data and illustrate that we can easily detect up to fifth degree cousins which was not possible using the existing methods. We also show in the 1000 genomes data with cryptic relationships that our method can detect these individuals.AvailabilityThe software is freely available for download at http://genetics.cs.ucla.edu/crypto/

Privacy-Preserving Clustering of Unstructured Big Data for Cloud-Based Enterprise Search Solutions

Cloud-based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient search solutions over their enterprise big datasets. However, individuals and businesses that deal with confidential big data (eg, credential documents) are reluctant to fully embrace such services, due to valid concerns about data privacy. Solutions based on client-side encryption have been explored to mitigate privacy concerns. Nonetheless, such solutions hinder data processing, specifically clustering, which is pivotal in dealing with different forms of big data. For instance, clustering is critical to limit the search space and perform real-time search operations on big datasets. To overcome the hindrance in clustering encrypted big data, we propose privacy-preserving clustering schemes for three forms of unstructured encrypted big datasets, namely static, semi-dynamic, and dynamic datasets. To preserve data privacy, the proposed clustering schemes function based on statistical characteristics of the data and determine (A) the suitable number of clusters and (B) appropriate content for each cluster. Experimental results obtained from evaluating the clustering schemes on three different datasets demonstrate between 30% to 60% improvement on the clusters' coherency compared to other clustering schemes for encrypted data. Employing the clustering schemes in a privacy-preserving enterprise search system decreases its search time by up to 78%, while increases the search accuracy by up to 35%.Comment: arXiv admin note: text overlap with arXiv:1908.0496

Analyzing the Privacy and Societal Challenges Stemming from the Rise of Personal Genomic Testing

Progress in genomics is enabling researchers to better understand the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. At the same time, the rapid cost drop of genome sequencing has enabled the emergence of a booming market for direct-to-consumer (DTC) genetic testing. Nowadays, companies like 23andMe and AncestryDNA provide affordable health, genealogy, and ancestry reports, and have already tested tens of millions of customers. How- ever, while this technology has the potential to transform society by improving people’s lives, it also harbors dangers as it prompts important privacy and societal concerns. In this thesis, we shed light on these issues using a mixed-methods approach. We start by conducting a technical investigation of the limitations on privacy-enhancing technologies used for testing, storing, and sharing genomic data. We rely on a structured methodology to contextualize and provide a critical analysis of the current state-of-the-art and we identify and discuss ten open problems faced by the community. We then focus on the societal aspects of DTC genetic testing by conducting two large-scale analyses of the genetic testing discourse focusing on both mainstream and fringe social networks, specifically, Twitter, Reddit, and 4chan. Our analyses show that DTC genetic testing is a popular topic of discussion on all platforms. However, these discussions often include highly toxic language expressed through hateful and racist comments and openly antisemitic rhetoric, often conveyed through memes. Overall, our findings highlight that the rise in popularity of this new technology is accompanied by several societal implications that are unlikely to be addressed by only one research field and rather require a multi-disciplinary approach

Identity, Law, and the Right to a Dream?

This paper engages critically with the new orthodoxy holding that individuals have a right to know their genetic origins and that such knowledge is crucial to realizing their identities. It examines two case studies: the Pratten litigation under the Canadian Charter of Rights and Freedoms regarding anonymous donor conception and scholarship approving a reform to Quebec\u27s adoption law. It addresses the supposed identity gap between those who are adopted or donor-conceived and those who are neither Arguments for law reform exaggerate that gap, opposing the incomplete, insecure identity of the adopted or donorconceived to the ostensibly complete, secure identity of those raised by their putatively genetic parents. A result is to overstate what is distinct and harmful about being adopted or donor-conceived. The paper also identifies a mistaken perception of law\u27s role in fashioning identity and recognizing family ties, including what law does for those who are not adopted or donor-conceived and what it might do for those who are. Some claims for law reform in the service of identity expect more from law than it can or should provide

New Statistical Transfer Learning Models for Health Care Applications

abstract: Transfer learning is a sub-field of statistical modeling and machine learning. It refers to methods that integrate the knowledge of other domains (called source domains) and the data of the target domain in a mathematically rigorous and intelligent way, to develop a better model for the target domain than a model using the data of the target domain alone. While transfer learning is a promising approach in various application domains, my dissertation research focuses on the particular application in health care, including telemonitoring of Parkinson’s Disease (PD) and radiomics for glioblastoma. The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD. The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature. The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations

The proliferation of large genomic databases offers the potential to perform increasingly larger-scale genome-wide association studies (GWASs). Due to privacy concerns, however, access to these data is limited, greatly reducing their usefulness for research. Here, we introduce a computational framework for performing GWASs that adapts principles of differential privacy-a cryptographic theory that facilitates secure analysis of sensitive data-to both protect private phenotype information (e.g., disease status) and correct for population stratification. This framework enables us to produce privacy-preserving GWAS results based on EIGENSTRAT and linear mixed model (LMM)-based statistics, both of which correct for population stratification. We test our differentially private statistics, PrivSTRAT and PrivLMM, on simulated and real GWAS datasets and find they are able to protect privacy while returning meaningful results. Our framework can be used to securely query private genomic datasets to discover which specific genomic alterations may be associated with a disease, thus increasing the availability of these valuable datasets.National Institutes of Health (U.S.) (Grant GM108348