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 mitigates iterated differential attacks proposed by Goodrich. Further an implementation of the system is evaluated and compared against current privacy-preserving string matching algorithms.
Alternatively, techniques from private set intersection (PSI) @cite_19 could be used. However, revealing the content of the intersection is not appropriate for a privacy preserving protocol. Based on these security concerns, protocols for private set intersection cardinality (PSI-CA) were developed @cite_0 . Yet, these solutions still reveal the intersection cardinality, whereas we only reveal whether there is a match.
@cite_0 @cite_19
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In many everyday scenarios, sensitive information must be shared between parties without complete mutual trust. Private set operations are particularly useful to enable sharing information with privacy, as they allow two or more parties to jointly compute operations on their sets (e.g., intersection, union, etc.), such that only the minimum required amount of information is disclosed. In the last few years, the research community has proposed a number of secure and efficient techniques for Private Set Intersection (PSI), however, somewhat less explored is the problem of computing the magnitude, rather than the contents, of the intersection – we denote this problem as Private Set Intersection Cardinality (PSI-CA). This paper explores a few PSI-CA variations and constructs several protocols that are more efficient than the state-of-the-art. The constantly increasing dependence on anytime-anywhere availability of data and the commensurately increasing fear of losing privacy motivate the need for privacy-preserving techniques. One interesting and common problem occurs when two parties need to privately compute an intersection of their respective sets of data. In doing so, one or both parties must obtain the intersection (if one exists), while neither should learn anything about other set elements. Although prior work has yielded a number of effective and elegant Private Set Intersection (PSI) techniques, the quest for efficiency is still underway. This paper explores some PSI variations and constructs several secure protocols that are appreciably more efficient than the state-of-the-art. Cryptographic protocols for Private Set Intersection (PSI) are the basis for many important privacy-preserving applications. Over the past few years, intensive research has been devoted to designing custom protocols for PSI based on homomorphic encryption and other public-key techniques, apparently due to the belief that solutions using generic approaches would be impractical. This paper explores the validity of that belief. We develop three classes of protocols targeted to different set sizes and domains, all based on Yao’s generic garbled-circuit method. We then compare the performance of our protocols to the fastest custom PSI protocols in the literature. Our results show that a careful application of garbled circuits leads to solutions that can run on million-element sets on typical desktops, and that can be competitive with the fastest custom protocols. Moreover, generic protocols like ours can be used directly for performing more complex secure computations, something we demonstrate by adding a simple information-auditing mechanism to our PSI protocols. Large scale data processing brings new challenges to the design of privacy-preserving protocols: how to meet the increasing requirements of speed and throughput of modern applications, and how to scale up smoothly when data being protected is big. Efficiency and scalability become critical criteria for privacy preserving protocols in the age of Big Data. In this paper, we present a new Private Set Intersection (PSI) protocol that is extremely efficient and highly scalable compared with existing protocols. The protocol is based on a novel approach that we call oblivious Bloom intersection. It has linear complexity and relies mostly on efficient symmetric key operations. It has high scalability due to the fact that most operations can be parallelized easily. The protocol has two versions: a basic protocol and an enhanced protocol, the security of the two variants is analyzed and proved in the semi-honest model and the malicious model respectively. A prototype of the basic protocol has been built. We report the result of performance evaluation and compare it against the two previously fastest PSI protocols. Our protocol is orders of magnitude faster than these two protocols. To compute the intersection of two million-element sets, our protocol needs only 41 seconds (80-bit security) and 339 seconds (256-bit security) on moderate hardware in parallel mode.