A privacy-preserving solution for compressed storage and selective retrieval of genomic data

In clinical genomics, the continuous evolution of bioinformatic algorithms and sequencing platforms makes it beneficial to store patients' complete aligned genomic data in addition to variant calls relative to a reference sequence. Due to the large size of human genome sequence data files (varying from 30 GB to 200 GB depending on coverage), two major challenges facing genomics laboratories are the costs of storage and the efficiency of the initial data processing. In addition, privacy of genomic data is becoming an increasingly serious concern, yet no standard data storage solutions exist that enable compression, encryption, and selective retrieval. Here we present a privacy-preserving solution named SECRAM (elective retrieval on Encrypted and Compressed Reference oriented Alignment Map) for the secure storage of compressed aligned genomic data. Our solution enables selective retrieval of encrypted data and improves the efficiency of downstream analysis (e.g., variant calling). Compared with BAM, the de facto standard for storing aligned genomic data, SECRAM uses 18% less storage. Compared with CRAM, one of the most compressed nonencrypted formats (using 34% less storage than BAM), SECRAM maintains efficient compression and downstream data processing, while allowing for unprecedented levels of security in genomic data storage. Compared with previous work, the distinguishing features of SECRAM are that (1) it is position-based instead of read-based, and (2) it allows random querying of a subregion from a BAM-like file in an encrypted form. Our method thus offers a space-saving, privacy-preserving, and effective solution for the storage of clinical genomic data.

Privacy-Enhancing Technologies for Medical Tests Using Genomic Data

Erman Ayday, Jean-Pierre Hubaux, Jean Louis Raisaro

In this paper, we propose privacy-enhancing technologies for medical tests and personalized medicine methods, which utilize patients' genomic data. First, we highlight the potential privacy threats on genomic data and the challenges of providing privacy-preserving algorithms. Then, focusing specifically on a typical disease-susceptibility test, we develop a new architecture (between the patient and the medical unit) and propose privacy-preserving algorithms by utilizing homomorphic encryption and proxy encryption. Assuming the whole genome sequencing is done by a certified institution, we propose to store patients' genomic data encrypted by their public keys at a Storage and Processing Unit (SPU). The proposed algorithm lets the SPU (or the medical unit) process the encrypted genomic data for medical tests and personalized medicine methods while preserving the privacy of patients' genomic data. We extensively analyze the relationship between the storage cost (of the genomic data), the level of genomic privacy (of the patient), and the characteristics of the genomic data. Furthermore, we implement and show via a complexity analysis the practicality of the proposed schemes. Finally, we evaluate the security of the proposed schemes and propose new research directions on genomic privacy.

2012

Computational analysis of ultraconserved non-coding DNA elements in vertebrate genomes

Slavica Dimitrieva Janeva

Recent advancements in DNA sequencing technologies, accompanied by parallel development of sophisticated computational methods, offer an unprecedented opportunity to explore and study the genetic material of many organisms. However, despite the tremendous progress in the field of genomics, our understanding of the genetic information encoded by the DNA is far from complete. Whole-genome comparisons of vertebrate species have uncovered the existence of non-coding DNA sequences that exhibit exceptionally high levels of similarity over hundreds of base pairs across distant species, referred to as ultraconserved non-coding elements (UCNEs). The existence of such sequences represents one of the biggest mysteries in current biology. Many UCNEs exhibit even stronger conservation than protein coding regions, but to date no molecular mechanism has been described that would require such a high degree of conservation over such long sequences. This thesis focuses on computational analysis of UCNEs across vertebrate genomes, with an ultimate goal to provide insights into the functioning of these elements and to unravel the reasons for their extreme conservation. Using computational approaches we studied the salient characteristics of UCNEs, and explored their genomic environment and their organization across genomes. Like previous studies, we observed that UCNEs are organized as large clusters around essential developmental genes, forming genomic regulatory blocks. Then we sought to understand the reasons behind this strong clustering of UCNEs. The clustering could reflect functional cooperativity between UCNEs. Alternatively, if each UCNE acts independently, their high concentration near developmental genes could merely reflect the extreme regulatory complexity of these genes. In a special setting of genomic context analysis, we analyzed the fate of UCNEs in teleost fish genomes that were subjected to an additional round of whole-genome duplication. We found that in most cases all UCNEs of a block were retained in one copy only, but together on the same chromosome. Conversely, the corresponding target genes were often retained in two copies, one completely devoid of UCNEs. Our results suggested that UCNEs of a cluster function in a highly cooperative manner. We propose that a multitude of cooperative cis-interactions between UCNEs is the reason for their extreme sequence conservation. Our work presents a novel hypothesis about the reasons for UCNE conservation and their mode of action, which is yet to be confirmed experimentally. The results from our study are made accessible through a new web resource called UCNEbase (http://ccg.vital-it.ch/UCNEbase). UCNEbase provides informa- tion about the genomic organization of UCNEs and their association with developmental regulatory genes, and enables all data to be explored in a genome browser environment. As part of a side project, we developed a new sparsified version of algorithms for computational prediction of RNA secondary structures and introduced a new program sibRNAfold. We performed a thorough analysis of the time complexity of sparsified RNA folding algorithms covering a large parameter space and empirically demonstrated that sparse RNA folding has cubic time complexity rather than quadratic, as claimed previously.

EPFL2013

Privacy-Enhancing Technologies for Medical and Genomic Data: From Theory to Practice

Jean Louis Raisaro

The impressive technological advances in genomic analysis and the significant drop in the cost of genome sequencing are paving the way to a variety of revolutionary applications in modern healthcare. In particular, the increasing understanding of the human genome, and of its relation to diseases, health and to responses to treatments brings promise of improvements in better preventive and personalized medicine. Unfortunately, the impact on privacy and security is unprecedented. The genome is our ultimate identifier and, if leaked, it can unveil sensitive and personal information such as our genetic diseases, our propensity to develop certain conditions (e.g., cancer or Alzheimer's) or the health issues of our family. Even though legislation, such as the EU General Data Protection Regulation (GDPR) or the US Health Insurance Portability and Accountability Act (HIPAA), aims at mitigating abuses based on genomic and medical data, it is clear that this information also needs to be protected by technical means. In this thesis, we investigate the problem of developing new and practical privacy-enhancing technologies (PETs) for the protection of medical and genomic data. Our goal is to accelerate the adoption of PETs in the medical field in order to address the privacy and security concerns that prevent personalized medicine from reaching its full potential. We focus on two main areas of personalized medicine: clinical care and medical research. For clinical care, we first propose a system for securely storing and selectively retrieving raw genomic data that is indispensable for in-depth diagnoses and treatments of complex genetic diseases such as cancer. Then, we focus on genetic variants and devise a new model based on additively-homomorphic encryption for privacy-preserving genetic testing in clinics. Our model, implemented in the context of HIV treatment, is the first to be tested and evaluated by practitioners in a real operational setting. For medical research, we first propose a method that combines somewhat-homomorphic encryption with differential privacy to enable secure feasibility studies on genetic data stored at an untrusted central repository. Second, we address the problem of sharing genomic and medical data when the data is distributed across multiple mistrustful institutions. We begin by analyzing the risks that threaten patientsâ privacy in systems for the discovery of genetic variants, and we propose practical mitigations to the re-identification risk. Then, for clinical sites to be able to share the data without worrying about the risk of data breaches, we develop a new system based on collective homomorphic encryption: it achieves trust decentralization and enables researchers to securely find eligible patients for clinical studies. Finally, we design a new framework, complementary to the previous ones, for quantifying the risk of unintended disclosure caused by potential inference attacks that are jointly combined by a malicious adversary, when exact genomic data is shared. In summary, in this thesis we demonstrate that PETs, still believed unpractical and immature, can be made practical and can become real enablers for overcoming the privacy and security concerns blocking the advancement of personalized medicine. Addressing privacy issues in healthcare remains a great challenge that will increasingly require long-term collaboration among geneticists, healthcare providers, ethicists, lawmakers, and computer scientists.