Efficient Server-Aided Secure Two-Party Function Evaluation with Applications to Genomic Computation

Abstract

Computation based on genomic data is becoming increasingly popular today, be it for medical or other purposes. Non-medical uses of genomic data in a computation often take place in a server-mediated setting where the server offers the ability for joint genomic testing between the users. Undeniably, genomic data is highly sensitive, which in contrast to other biometry types, discloses a plethora of information not only about the data owner, but also about his or her relatives. Thus, there is an urgent need to protect genomic data. This is particularly true when the data is used in computation for what we call recreational non-health-related purposes. Towards this goal, in this work we put forward a framework for server-aided secure two-party computation with the security model motivated by genomic applications. One particular security setting that we treat in this work provides stronger security guarantees with respect to malicious users than the traditional malicious model. In particular, we incorporate certified inputs into secure computation based on garbled circuit evaluation to guarantee that a malicious user is unable to modify her inputs in order to learn unauthorized information about the other user’s data. Our solutions are general in the sense that they can be used to securely evaluate arbitrary functions and offer attractive performance compared to the state of the art. We apply the general constructions to three specific types of genomic tests: paternity, genetic compatibility, and ancestry testing and implement the constructions. The results show that all such private tests can be executed within a matter of seconds or less despite the large size of one’s genomic data.

Privado

“…Parties only learn the output of function and nothing else. The main issue in S-2PC protocols is that the two parties are required to be online and involved per function evaluation to communicate some secret information [26,28,20,33,24,9,5]. This is in contrast to the advertising transparency.…”

The Case of Adversarial Inputs for Secure Similarity Approximation Protocols

“…Notice that if the adversary were to corrupt both a client and the server then she would have access to the common random input, and thus become capable of mounting a perturbation attack. We note here that the server-aided approach has been successfully deployed [11], [54], [55] in various other problems. The proposed perturbation attacks of the previous Section are based on the fact that all clients have offline and direct access to the common random input r cmn .…”

“…The seminal work by Feigenbaum et al [35] set the foundation for secure multiparty computation of approximation functions. Furthermore, the community has made several important steps towards private computation on genomic data in a time-efficient and scalable manner [6], [11], [24], [29], [73]. Wang et al [87] demonstrate the potential of secure approximations, by running a privacy-preserving similarity query for a human genome on 1 million records distributed across the U.S., in a couple of minutes.…”

PPAD: Privacy Preserving Group-Based ADvertising in Online Social Networks

“…Server-assisted 2PC solutions either employ a server to guarantee the fairness of the protocol execution [13], [22], [24] or to ease the other parties' duties by delivering the main computation overhead to the server. However, users and advertisers are required to be online and provide some information per function evaluation (advertisement in this case) [17], [19], [13], [25], [15], [9], [6]. [29] proposed a solution which mitigates the necessity of online users and advertisers by applying two servers, similar to our approach.…”