Due to its hereditary nature, genomic data is not only linked to its owner but to that of close relatives as well. As a result, its sensitivity does not really degrade over time; in fact, the relevance of a genomic sequence is likely to be longer than the security provided by encryption. This prompts the need for specialized techniques providing long-term security for genomic data, yet the only available tool for this purpose is GenoGuard (, 2015). By relying on Honey Encryption, GenoGuard is secure against an adversary that can brute force all possible keys; i.e., whenever an attacker tries to decrypt using an incorrect password, she will obtain an incorrect but plausible looking decoy sequence. In this paper, we set to analyze the real-world security guarantees provided by GenoGuard; specifically, assess how much more information does access to a ciphertext encrypted using GenoGuard yield, compared to one that was not. Overall, we find that, if the adversary has access to side information in the form of partial information from the target sequence, the use of GenoGuard does appreciably increase her power in determining the rest of the sequence. We show that, in the case of a sequence encrypted using an easily guessable (low-entropy) password, the adversary is able to rule out most decoy sequences, and obtain the target sequence with just 2.5 of it available as side information. In the case of a harder-to-guess (high-entropy) password, we show that the adversary still obtains, on average, better accuracy in guessing the rest of the target sequences than using state-of-the-art genomic sequence inference methods, obtaining up to 15 improvement in accuracy.
Data sharing. Progress in genomics research is dependent on collaboration and data sharing among different institutions. Given the sensitive nature of the data, as well as regulatory and ethics constraints, this often proves to be a challenging task. @cite_4 propose the use of secret sharing to distribute data among several entities and, using secure multi-party computations, support privacy-friendly computations across multiple entities. @cite_3 present GENSETS, a genome-wide, privacy-preserving similar patients querying system using genomic edit distance approximation and private set difference protocols. Then, @cite_21 use Software Guard Extensions (SGX) to build a privacy-preserving international collaboration tool; this enables secure and distributed computations over encrypted data, thus supporting the analysis of rare disease genetic data across different continents. Finally, Oprisanu and De Cristofaro @cite_14 present a framework ( AnoniMME'') geared supporting anonymous queries within the Matchmaker Exchange platform, which allows researchers to perform queries for rare genetic disease discovery over multiple federated databases.
@cite_14 @cite_21 @cite_4 @cite_3
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Motivation: Advances in genome sequencing and genomics research are bringing us closer to a new era of personalized medicine, where healthcare can be tailored to the individual’s genetic makeup, and to more effective diagnosis and treatment of rare genetic diseases. Much of this progress depends on collaborations and access to genomes, and thus a number of initiatives have been introduced to support seamless data sharing. Among these, the Global Alliance for Genomics and Health runs a popular platform, called Matchmaker Exchange, which allows researchers to perform queries for rare genetic disease discovery over multiple federated databases. Queries include gene variations which are linked to rare diseases, and the ability to find other researchers that have seen or have interest in those variations is extremely valuable. Nonetheless, in some cases, researchers may be reluctant to use the platform since the queries they make (thus, what they are working on) are revealed to other researchers, and this creates concerns with privacy and competitive advantage. Contributions: We present AnoniMME, a novel framework geared to enable anonymous queries within the Matchmaker Exchange platform. The framework, building on a cryptographic primitive called Reverse Private Information Retrieval (PIR), let researchers anonymously query the federated platform, in a multi-server setting. Specifically, they write their query, along with a public encryption key, anonymously in a public database. Responses are also supported, so that other researchers can respond to queries by providing their encrypted contact details. Availability and Implementation: https: github.com bristena-op AnoniMME. Genomic datasets are often associated with sensitive phenotypes. Therefore, the leak of membership information is a major privacy risk. Genomic beacons aim to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes no queries on the presence of specific alleles in the dataset. Previously deemed secure against re-identification attacks, beacons were shown to be vulnerable despite their stringent policy. Recent studies have demonstrated that it is possible to determine whether the victim is in the dataset, by repeatedly querying the beacon for his her single nucleotide polymorphisms (SNPs). In this work, we propose a novel re-identification attack and show that the privacy risk is more serious than previously thought. Using the proposed attack, even if the victim systematically hides informative SNPs (i.e., SNPs with very low minor allele frequency -MAF-), it is possible to infer the alleles at positions of interest as well as the beacon query results with very high confidence. Our method is based on the fact that alleles at different loci are not necessarily independent. We use the linkage disequilibrium and a high-order Markov chain-based algorithm for the inference. We show that in a simulated beacon with 65 individuals from the CEU population, we can infer membership of individuals with 95 confidence with only 5 queries, even when SNPs with MAF less than 0.05 are hidden. This means, we need less than 0.5 of the number of queries that existing works require, to determine beacon membership under the same conditions. We further show that countermeasures such as hiding certain parts of the genome or setting a query budget for the user would fail to protect the privacy of the participants under our adversary model. To support large-scale biomedical research projects, organizations need to share person-specific genomic sequences without violating the privacy of their data subjects. In the past, organizations protected subjects' identities by removing identifiers, such as name and social security number; however, recent investigations illustrate that deidentified genomic data can be ldquoreidentifiedrdquo to named individuals using simple automated methods. In this paper, we present a novel cryptographic framework that enables organizations to support genomic data mining without disclosing the raw genomic sequences. Organizations contribute encrypted genomic sequence records into a centralized repository, where the administrator can perform queries, such as frequency counts, without decrypting the data. We evaluate the efficiency of our framework with existing databases of single nucleotide polymorphism (SNP) sequences and demonstrate that the time needed to complete count queries is feasible for real world applications. For example, our experiments indicate that a count query over 40 SNPs in a database of 5000 records can be completed in approximately 30 min with off-the-shelf technology. We further show that approximation strategies can be applied to significantly speed up query execution times with minimal loss in accuracy. The framework can be implemented on top of existing information and network technologies in biomedical environments. Purpose: Sharing study data within the research community generates tension between two important goods: promoting scientific goals and protecting the privacy interests of study participants. This study was designed to explore the perceptions, beliefs, and attitudes of research participants and possible future participants regarding genome-wide association studies and repository-based research. Methods: Focus group sessions with (1) current research participants, (2) surrogate decision-makers, and (3) three age-defined cohorts (18‐34 years, 35‐ 50, 50). Results: Participants expressed a variety of opinions about the acceptability of wide sharing of genetic and phenotypic information for research purposes through large, publicly accessible data repositories. Most believed that making de-identified study data available to the research community is a social good that should be pursued. Privacy and confidentiality concerns were common, although they would not necessarily preclude participation. Many participants voiced reservations about sharing data with for-profit organizations. Conclusions: Trust is central in participants’ views regarding data sharing. Further research is needed to develop governance models that enact the values of stewardship. Genet Med 2010:12(8):486–495.