Publications by Erman Ayday | EPFL Graph Search

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Erman Ayday, Jean-Pierre Hubaux, Apostolos Pyrgelis, Juan Ramón Troncoso-Pastoriza

2022

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

Erman Ayday, Jacques Fellay, Zhicong Huang, Jean-Pierre Hubaux, Huang Lin, Adam Molyneaux, Zhi Ming Xu

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.

Cold Spring Harbor Laboratory2016

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A privacy-preserving method for performing a disease susceptibility test on a patient, said method comprising: (I) performing homomorphic computations, (J) obtaining a test result which is partly decrypted with a first part (prk1 resp. prk2) of a private key, (L) decrypting said partly decrypted result with a second part (prk2 resp. prk1) of said private key, wherein said homomorphic computations are based on encrypted genomic markers of the patient, on encrypted clinical and/or environmental markers, and on encrypted ancestry markers of the patient. The invention is also related to a method for inferring ancestry in the encrypted domain.

2016

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With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists need large collections of human genomes to further increase this speed. Furthermore, individuals are using their genomes to learn about their (genetic) predispositions to diseases, their ancestries, and even their (genetic) compatibilities with potential partners. This trend has also caused the launch of health-related websites and online social networks (OSNs), in which individuals share their genomic data (e.g., Open-SNP or 23andMe). On the other hand, genomic data carries much sensitive information about its owner. By analyzing the DNA of an individual, it is now possible to learn about his disease predispositions (e.g., for Alzheimer's or Parkinson's), ancestries, and physical attributes. The threat to genomic privacy is magnified by the fact that a person's genome is correlated to his family members' genomes, thus leading to interdependent privacy risks. This short tutorial will help computer scientists better understand the privacy and security challenges in today's genomic era. We will first highlight the significance of genomic data and the threats for genomic privacy. Then, we will present the high level descriptions of the proposed solutions to protect the privacy of genomic data and we will discuss future research directions. No prerequisite knowledge on biology or genomics is required for the attendees of this proposal. We only require the attendees to have a slight background on cryptography and statistics.

Assoc Computing Machinery2016

Privacy-enhancing technologies for medical tests using genomic data

Erman Ayday, Jacques Fellay, Jean-Pierre Hubaux, Mathias Jacques Jean-Marc Humbert, Jean Louis Raisaro, Jacques Rougemont, Amalio Telenti

In this invention, we propose privacy-enhancing technologies for medical tests and personalized medicine methods, which utilize patients' genomic data. Assuming the whole genome sequencing is done by a certified institution, we propose to store patients' genomic data encrypted by a patient's public keys at a Storage and Processing Unit (SPU). A part of the corresponding private key is also stored on the SPU. At the time of the test by a Medical Unit (MU), the patient provides the second part of the private key to the MU. A test with its associated markers is determined by the MU and sent to the SPU. The test is carried out on the encrypted values thanks to homomorphic operation and returned back to the MU. The latter uses the second part of the private key to access the result.

2016

Privacy-preserving genomic testing in the clinic: a model using HIV treatment

Erman Ayday, Istvan Bartha, Jacques Fellay, Jean-Pierre Hubaux, Jean Louis Raisaro, Amalio Telenti

The implementation of genomic-based medicine is hindered by unresolved questions regarding data privacy and delivery of interpreted results to health-care practitioners. We used DNA-based prediction of HIV-related outcomes as a model to explore critical issues in clinical genomics.

Nature Publishing Group2016

De-anonymizing Genomic Databases Using Phenotypic Traits

Erman Ayday, Jean-Pierre Hubaux, Joachim Ludovic Hugonot, Kévin Clément Huguenin, Mathias Jacques Jean-Marc Humbert

People increasingly have their genomes sequenced and some of them share their genomic data online. They do so for various purposes, including to find relatives and to help advance genomic research. An individual's genome carries very sensitive, private information such as its owner's susceptibility to diseases, which could be used for discrimination. Therefore, genomic databases are often anonymized. However, an individual's genotype is also linked to visible phenotypic traits, such as eye or hair color, which can be used to re-identify users in anonymized public genomic databases, thus raising severe privacy issues. For instance, an adversary can identify a target's genome using known her phenotypic traits and subsequently infer her susceptibility to Alzheimer's disease. In this paper, we quantify, based on various phenotypic traits, the extent of this threat in several scenarios by implementing de-anonymization attacks on a genomic database of OpenSNP users sequenced by 23andMe. Our experimental results show that the proportion of correct matches reaches 23% with a supervised approach in a database of 50 participants. Our approach outperforms the baseline by a factor of four, in terms of the proportion of correct matches, in most scenarios. We also evaluate the adversary's ability to predict individuals' predisposition to Alzheimer's disease, and we observe that the inference error can be halved compared to the baseline. We also analyze the effect of the number of known phenotypic traits on the success rate of the attack. As progress is made in genomic research, especially for genotype-phenotype associations, the threat presented in this paper will become more serious.

2015

Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies

Erman Ayday, Zhicong Huang, Jean-Pierre Hubaux, Florian Tramèr

Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy guarantees than traditional statistical methods. However, recent studies have shown that for reasonable privacy budgets, differential privacy significantly affects the expected utility. Many alternative privacy notions which aim at relaxing DP have since been proposed, with the hope of providing a better tradeoff between privacy and utility. At CCS’13, Li et al. introduced the membership privacy framework, wherein they aim at protecting against set membership disclosure by adversaries whose prior knowledge is captured by a family of probability distributions. In the context of this framework, we investigate a relaxation of DP, by considering prior distributions that capture more reasonable amounts of background knowledge. We show that for different privacy budgets, DP can be used to achieve membership privacy for various adversarial settings, thus leading to an interesting tradeoff between privacy guarantees and utility. We re-evaluate methods for releasing differentially private chi^2-statistics in genome-wide association studies and show that we can achieve a higher utility than in previous works, while still guaranteeing membership privacy in a relevant adversarial setting.

2015

GenoGuard: Protecting Genomic Data against Brute-Force Attacks

Erman Ayday, Jacques Fellay, Zhicong Huang, Jean-Pierre Hubaux, Ari Juels

Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been aggravating the potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term, but given the multi-decade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool, called {\em GenoGuard}, for providing strong protection for genomic data both today and in the long term. GenoGuard incorporates a new theoretical framework for encryption called honey encryption (HE): it can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can be applied to messages from, unfortunately, a very restricted set of probability distributions. Therefore, GenoGuard addresses the open problem of applying HE techniques to the highly non-uniform probability distributions that characterize sequences of genetic data. In GenoGuard, a potential adversary can attempt exhaustively to guess keys or passwords and decrypt via a brute-force attack. We prove that decryption under any key will yield a plausible genome sequence, and that GenoGuard offers an information-theoretic security guarantee against message-recovery attacks. We also explore attacks that use side information. Finally, we present an efficient and parallelized software implementation of GenoGuard.

2015

GenoGuard: Protecting Genomic Data Against Brute-Force Attacks

Erman Ayday, Jacques Fellay, Zhicong Huang, Jean-Pierre Hubaux, Ari Juels

Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been aggravating the potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term; given the multi-decade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool called {\em GenoGuard} to provide strong protection for genomic data both today and in the long term. GenoGuard incorporates a new theoretical framework for encryption called honey encryption (HE) that can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can unfortunately be applied to messages from only a very restricted set of probability distributions. GenoGuard thus addresses the open problem of applying HE techniques to the highly non-uniform probability distributions characterizing sequences of genetic data. In GenoGuard, a potential adversary can attempt to guess keys or passwords exhaustively and decrypt via a brute-force attack. We prove that decryption under any key, however, will yield a plausible genome sequence; thus GenoGuard offers an information-theoretic security guarantee against message-recovery attacks. We also explore attacks using side information. Finally, we present an efficient and parallelized software implementation of GenoGuard.

2015