Record linkage has been extensively used in various data mining applications involving sharing data. While the amount of available data is growing, the concern of disclosing sensitive information poses the problem of utility vs privacy. In this paper, we study the problem of private record linkage via secure data transformations. In contrast to the existing techniques in this area, we propose a novel approach that provides strong privacy guarantees under the formal framework of differential privacy. We develop an embedding strategy based on frequent variable length grams mined in a private way from the original data. We also introduce personalized threshold for matching individual records in the embedded space which achieves better linkage accuracy than the existing global threshold approach. Compared with the state-of-the-art secure matching schema, our approach provides formal, provable privacy guarantees and achieves better scalability while providing comparable utility.
Secure Multiparty Computation (SMC) techniques cast the record linkage problem into a secure communication framework. In this scenario, several parties are involved in the protocol where the communication is done using cryptography. The key idea is that the computation itself should reveal no more than whatever may be revealed by examining the input and output of each party. An important theoretical result in the cryptographic area @cite_7 shows that any computational functions can be computed in this setting. Motivate by this, several works have been proposed in the literature. For example, when the exact match is considered the record linkage problem can be interpreted as a set intersection problem @cite_26 . To mention, the work in @cite_2 investigates the SMC approach in privacy preserving data mining. While in principle the private record linkage problem can be solved using SMC and cryptography, the computational and communication cost of these methods turn out too great in real application.
@cite_26 @cite_7 @cite_2
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In secure multi-party computations (SMC), parties wish to compute a function on their private data without revealing more information about their data than what the function reveals. In this paper, we investigate two Shannontype questions on this problem. We first consider the traditional one-shot model for SMC which does not assume a probabilistic prior on the data. In this model, private communication and randomness are the key enablers to secure computing, and we investigate a notion of randomness cost and capacity. We then move to a probabilistic model for the data, and propose a Shannon model for discrete memoryless SMC. In this model, correlations among data are the key enablers for secure computing, and we investigate a notion of dependency which permits the secure computation of a function. While the models and questions are general, this paper focuses on summation functions and relies on polar code constructions. The growth of the Internet has triggered tremendous opportunities for cooperative computation, where people are jointly conducting computation tasks based on the private inputs they each supplies. These computations could occur between mutually untrusted parties, or even between competitors. For example, customers might send to a remote database queries that contain private information; two competing financial organizations might jointly invest in a project that must satisfy both organizations' private and valuable constraints, and so on. Today, to conduct such computations, one entity must usually know the inputs from all the participants; however if nobody can be trusted enough to know all the inputs, privacy will become a primary concern.This problem is referred to as Secure Multi-party Computation Problem (SMC) in the literature. Research in the SMC area has been focusing on only a limited set of specific SMC problems, while privacy concerned cooperative computations call for SMC studies in a variety of computation domains. Before we can study the problems, we need to identify and define the specific SMC problems for those computation domains. We have developed a framework to facilitate this problem-discovery task. Based on our framework, we have identified and defined a number of new SMC problems for a spectrum of computation domains. Those problems include privacy-preserving database query, privacy-preserving scientific computations, privacy-preserving intrusion detection, privacy-preserving statistical analysis, privacy-preserving geometric computations, and privacy-preserving data mining.The goal of this paper is not only to present our results, but also to serve as a guideline so other people can identify useful SMC problems in their own computation domains. In the setting of secure two-party computation, two parties wish to securely compute a joint function of their private inputs, while revealing only the output. One of the primary techniques for achieving efficient secure two-party computation is that of Yao’s garbled circuits (FOCS 1986). In the semi-honest model, where just one garbled circuit is constructed and evaluated, Yao’s protocol has proven itself to be very efficient. However, a malicious adversary who constructs the garbled circuit may construct a garbling of a different circuit computing a different function, and this cannot be detected (due to the garbling). In order to solve this problem, many circuits are sent and some of them are opened to check that they are correct while the others are evaluated. This methodology, called cut-and-choose, introduces significant overhead, both in computation and in communication, and is mainly due to the number of circuits that must be used in order to prevent cheating. Private record linkage protocols allow multiple parties to exchange matching records, which refer to the same entities or have similar values, while keeping the non-matching ones secret. Conventional protocols are based on computationally expensive cryptographic primitives and therefore do not scale. To address these scalability issues, hybrid protocols have been proposed that combine differential privacy techniques with secure multiparty computation techniques. However, a drawback of such protocols is that they disclose to the parties both the matching records and the differentially private synopses of the datasets involved in the linkage. Consequently, differential privacy is no longer always satisfied. To address this issue, we propose a novel framework that separates the private synopses from the matching records. The two parties do not access the synopses directly, but still use them to efficiently link records. We theoretically prove the security of our framework under the state-of-the-art privacy notion of differential privacy for record linkage (DPRL). In addition, we develop a simple but effective strategy for releasing private synopses. Extensive experimental results show that our framework is superior to the existing methods in terms of efficiency.