Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

THaW publications

In 2013, the National Science Foundation\u27s Secure and Trustworthy Cyberspace program awarded a Frontier grant to a consortium of four institutions, led by Dartmouth College, to enable trustworthy cybersystems for health and wellness. As of this writing, the Trustworthy Health and Wellness (THaW) project\u27s bibliography includes more than 130 significant publications produced with support from the THaW grant; these publications document the progress made on many fronts by the THaW research team. The collection includes dissertations, theses, journal papers, conference papers, workshop contributions and more. The bibliography is organized as a Zotero library, which provides ready access to citation materials and abstracts and associates each work with a URL where it may be found, cluster (category), several content tags, and a brief annotation summarizing the work\u27s contribution. For more information about THaW, visit thaw.org

A New Approach for Homomorphic Encryption with Secure Function Evaluation on Genomic Data

Additively homomorphic encryption is a public-key primitive allowing a sum to be computed on encrypted values. Although limited in functionality, additive schemes have been an essential tool in the private function evaluation toolbox for decades. They are typically faster and more straightforward to implement relative to their fully homomorphic counterparts, and more efficient than garbled circuits in certain applications. This thesis presents a novel method for extending the functionality of additively homomorphic encryption to allow the private evaluation of functions of restricted domain. Provided the encrypted sum falls within the restricted domain, the function can be homomorphically evaluated “for free” in a single public-key operation. We will describe an algorithm for encoding private functions into the public-keys of two well-known additive cryptosystems. We extend this scheme to an application in the field of pharmacogenomics called Similar Patient Query. With the advent of human genome project, there is a tremendous availability of genomic data opening the door for a possibility of many advances in the field of medicine. Precision medicine is one such application where a patient is administered drugs based on their genetic makeup. If the genomic data is not kept private, it can lead to several information frauds, so it needs to be encrypted. To tap the full potential of the encrypted genomic data, we need to perform computations on it without compromising its security. For SPQ, we pick a query genome and compare it across a hospital data base, to find patients similar to that of the query and use the information to apply precision medicine, all of this is carried out under privacy preserving settings in the presence of a semi-honest adversary in a single transaction