Privacy is of the utmost importance in genomic matching. Therefore a number of privacy-preserving protocols have been presented using secure computation. Nevertheless, none of these protocols prevents inferences from the result. Goodrich has shown that this resulting information is sufficient for an effective attack on genome databases. In this paper we present an approach that can detect and mitigate such an attack on encrypted messages while still preserving the privacy of both parties. Note that randomization, e.g. using differential privacy, will almost certainly destroy the utility of the matching result. We combine two known cryptographic primitives -- secure computation of the edit distance and fuzzy commitments -- in order to prevent submission of similar genome sequences. Particularly, we contribute an efficient zero-knowledge proof that the same input has been used in both primitives. We show that using our approach it is feasible to preserve privacy in genome matching and also detect and mitigate Goodrich's attack.
It is often necessary for two or more or more parties that do not fully trust each other to share data selectively. For example, one intelligence agency might be willing to turn over certain documents to another such agency, but only if the second agency requests the specific documents. The problem, of course, is finding out that such documents exist when access to the database is restricted. We propose a search scheme based on Bloom filters and group ciphers such as Pohlig-Hellman encryption. A semi-trusted third party can transform one party’s search queries to a form suitable for querying the other party’s database, in such a way that neither the third party nor the database owner can see the original query. Furthermore, the encryption keys used to construct the Bloom filters are not shared with this third party. Multiple providers and queriers are supported; provision can be made for third-party “warrant servers”, as well as “censorship sets” that limit the data to be shared. For over fifty years, "record linkage" procedures have been refined to integrate data in the face of typographical and semantic errors. These procedures are traditionally performed over personal identifiers (e.g., names), but in modern decentralized environments, privacy concerns have led to regulations that require the obfuscation of such attributes. Various techniques have been proposed to resolve the tension, including secure multi-party computation protocols, however, such protocols are computationally intensive and do not scale for real world linkage scenarios. More recently, procedures based on Bloom filter encoding (BFE) have gained traction in various applications, such as healthcare, where they yield highly accurate record linkage results in a reasonable amount of time. Though promising, no formal security analysis has been designed or applied to this emerging model, which is of concern considering the sensitivity of the corresponding data. In this paper, we introduce a novel attack, based on constraint satisfaction, to provide a rigorous analysis for BFE and guidelines regarding how to mitigate risk against the attack. In addition, we conduct an empirical analysis with data derived from public voter records to illustrate the feasibility of the attack. Our investigations show that the parameters of the BFE protocol can be configured to make it relatively resilient to the proposed attack without significant reduction in record linkage performance. Genome-wide association studies (GWAS) aim at discovering the association between genetic variations, particularly single-nucleotide polymorphism (SNP), and common diseases, which is well recognized to be one of the most important and active areas in biomedical research. Also renowned is the privacy implication of such studies, which has been brought into the limelight by the recent attack proposed by Homer's attack demonstrates that it is possible to identify a GWAS participant from the allele frequencies of a large number of SNPs. Such a threat, unfortunately, was found in our research to be significantly understated. In this paper, we show that individuals can actually be identified from even a relatively small set of statistics, as those routinely published in GWAS papers. We present two attacks. The first one extends Homer's attack with a much more powerful test statistic, based on the correlations among different SNPs described by coefficient of determination (r2). This attack can determine the presence of an individual from the statistics related to a couple of hundred SNPs. The second attack can lead to complete disclosure of hundreds of participants' SNPs, through analyzing the information derived from published statistics. We also found that those attacks can succeed even when the precisions of the statistics are low and part of data is missing. We evaluated our attacks on the real human genomes and concluded that such threats are completely realistic.