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

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby ???tag??? variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation

Privacy-Preserving Exploration of Genetic Cohorts with i2b2 At Lausanne University Hospital

Re-use of patients’ health records can provide tremendous benefits for clinical research. One of the first essential steps for many research studies, such as clinical trials or population health studies, is to effectively identify, from electronic health record systems, groups of well-characterized patients who meet specific inclusion and exclusion criteria. This procedure is called cohort exploration. Yet, when researchers need to compile specific cohorts of patients, privacy issues represent one of the major obstacles to accessing patients’ data, especially when sensitive data, such as genomic data, are involved. Because of this, cohort exploration could become extremely difficult and time-consuming. In this joint paper between the Ecole Polytechnique F ´ ed´ erale de Lausanne (EPFL) and the Lausanne University Hospital ´ (CHUV), we address the challenge of designing and deploying an efficient privacy-preserving explorer for genetic cohorts. Our solution is built on top of i2b2 (informatics for integrating biology and the bedside), the state-of-the-art open-source framework for cohort exploration, and exploits on cutting-edge privacy-enhancing technologies (PETs) such as homomorphic encryption and differential privacy. To the best of our knowledge, our proposed solution is the first of its kind to be successfully deployed in a real operational environment within a hospital. Especially, it has been tested as one of the services of the clinical research data-warehouse of CHUV. Solutions involving homomorphic encryption are often believed to be costly and still immature for use in operational environments. In this paper, we prove the opposite by describing how actually, for specific use cases, this kind of PETs can be very efficient enablers