Abstract
Secure multiparty computation allows for a set of users to evaluate a particular function over their inputs without revealing the information they possess to each other. Theoretically, this can be achieved using fully homomorphic encryption systems, but so far they remain in the realm of computational impracticability. An alternative is to consider secure function evaluation using homomorphic public-key cryptosystems or Garbled Circuits, the latter being a popular trend in recent times due to important breakthroughs. We propose a technique for computing the logsum operation using Garbled Circuits. This technique relies on replacing the logsum operation with an equivalent piecewise linear approximation, taking advantage of recent advances in efficient methods for both designing and implementing Garbled Circuits. We elaborate on how all the required blocks should be assembled in order to obtain small errors regarding the original logsum operation and very fast execution times.
Highlights
An increasing number of server-based applications perform tasks such as classification, processing and analysis of user data
We start by describing how the logsum operation can be computed using a piecewise linear approximation, we describe the full circuit for the simple situation where there are only two input values, and we generalize it to any number of input values
This paper presents a technique for computing the logsum operation using Garbled Circuits
Summary
An increasing number of server-based applications perform tasks such as classification, processing and analysis of user data. Often, these data are private, and should not be exposed to the server. The server’s inputs to these computations are private and may not be exposed to the user. It becomes necessary to perform the computations in a privacy preserving manner, such that the user’s data and the server’s inputs are not revealed to one another, while yet ensuring that the appropriate party gets the correct result from the computations. Alice desires to obtain f(x;θ) from Bob; she is unwilling to expose x to him. The challenge of privacy-preserving computation is to enable Alice to obtain f(x;θ) such that Bob learns nothing of x and Alice learns nothing more of θ besides what she may glean from f(x;θ) itself
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