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.
Although secure computation offers a formal security model -- semi-honest security @cite_2 , it does not prevent inferences from the result. Goodrich therefore presented an attack based on the information of the edit distance alone @cite_8 . With very few repeated queries it can infer real-world sized genomes. The contribution of this paper is to combine the approach of @cite_30 with mitigation of this and similar attacks.
@cite_30 @cite_8 @cite_2
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Consider two parties who want to compare their strings, e.g., genomes, but do not want to reveal them to each other. We present a system for privacy-preserving matching of strings, which differs from existing systems by providing a deterministic approximation instead of an exact distance. It is efficient (linear complexity), non-interactive and does not involve a third party which makes it particularly suitable for cloud computing. We extend our protocol, such that it only reveals whether there is a match and not the exact distance. Further an implementation of the system is evaluated and compared against current privacy-preserving string matching algorithms. In this paper, we study the degree to which a genomic string, @math ,leaks details about itself any time it engages in comparison protocolswith a genomic querier, Bob, even if those protocols arecryptographically guaranteed to produce no additional information otherthan the scores that assess the degree to which @math matches stringsoffered by Bob. We show that such scenarios allow Bob to play variantsof the game of Mastermind with @math so as to learn the complete identityof @math . We show that there are a number of efficient implementationsfor Bob to employ in these Mastermind attacks, depending on knowledgehe has about the structure of @math , which show how quickly he candetermine @math . Indeed, we show that Bob can discover @math using anumber of rounds of test comparisons that is much smaller than thelength of @math , under various assumptions regarding the types of scoresthat are returned by the cryptographic protocols and whether he can useknowledge about the distribution that @math comes from, e.g., usingpublic knowledge about the properties of human DNA. We also providethe results of an experimental study we performed on a database ofmitochondrial DNA, showing the vulnerability of existing real-world DNAdata to the Mastermind attack. Cryptography is concerned with the conceptualization, definition, and construction of computing systems that address security concerns. The design of cryptographic systems must be based on firm foundations. Building on the basic tools presented in the first volume, this second volume of Foundations of Cryptography contains a rigorous and systematic treatment of three basic applications: Encryption, Signatures, and General Cryptographic Protocols. It is suitable for use in a graduate course on cryptography and as a reference book for experts. The author assumes basic familiarity with the design and analysis of algorithms; some knowledge of complexity theory and probability is also useful. Also available: Volume I: Basic Tools 0-521-79172-3 Hardback $75.00 C