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.
In this paper we introduce a new tool for controlling the knowledge transfer process in cryptographic protocol design. It is applied to solve a general class of problems which include most of the two-party cryptographic problems in the literature. Specifically, we show how two parties A and B can interactively generate a random integer N = p?q such that its secret, i.e., the prime factors (p, q), is hidden from either party individually but is recoverable jointly if desired. This can be utilized to give a protocol for two parties with private values i and j to compute any polynomially computable functions f(i,j) and g(i,j) with minimal knowledge transfer and a strong fairness property. As a special case, A and B can exchange a pair of secrets sA, sB, e.g. the factorization of an integer and a Hamiltonian circuit in a graph, in such a way that sA becomes computable by B when and only when sB becomes computable by A. All these results are proved assuming only that the problem of factoring large intergers is computationally intractable. Two millionaires wish to know who is richer; however, they do not want to find out inadvertently any additional information about each other’s wealth. How can they carry out such a conversation? This is a special case of the following general problem. Suppose m people wish to compute the value of a function f(x1, x2, x3, . . . , xm), which is an integer-valued function of m integer variables xi of bounded range. Assume initially person Pi knows the value of xi and no other x’s. Is it possible for them to compute the value of f , by communicating among themselves, without unduly giving away any information about the values of their own variables? The millionaires’ problem corresponds to the case when m = 2 and f(x1, x2) = 1 if x1 < x2, and 0 otherwise. In this paper, we will give precise formulation of this general problem and describe three ways of solving it by use of one-way functions (i.e., functions which are easy to evaluate but hard to invert). These results have applications to secret voting, private querying of database, oblivious negotiation, playing mental poker, etc. We will also discuss the complexity question “How many bits need to be exchanged for the computation”, and describe methods to prevent participants from cheating. Finally, we study the question “What cannot be accomplished with one-way functions”. Before describing these results, we would like to put this work in perspective by first considering a unified view of secure computation in the next section. In this paper, we survey the basic paradigms and notions of secure mul- tiparty computation and discuss their relevance to the fleld of privacy-preserving data mining. In addition to reviewing deflnitions and constructions for secure mul- tiparty computation, we discuss the issue of e-ciency and demonstrate the di-cul- ties involved in constructing highly e-cient protocols. We also present common errors that are prevalent in the literature when secure multiparty computation techniques are applied to privacy-preserving data mining. Finally, we discuss the relationship between secure multiparty computation and privacy-preserving data mining, and show which problems it solves and which problems it does not.