Paper 2021/413

Blind Polynomial Evaluation and Data Trading

Yi Liu, Qi Wang, and Siu-Ming Yiu

Abstract

Data trading is an emerging business, in which data sellers provide buyers with, for example, their private datasets and get paid from buyers. In many scenarios, sellers prefer to sell pieces of data, such as statistical results derived from the dataset, rather than the entire dataset. Meanwhile, buyers wish to hide the results they retrieve. Since it is not preferable to rely on a trusted third party (TTP), we are wondering, in the absence of TTP, whether there exists a \emph{practical} mechanism satisfying the following requirements: the seller Sarah receives the payment if and only if she \emph{obliviously} returns the buyer Bob the \emph{correct} evaluation result of a function delegated by Bob on her dataset, and Bob can only derive the result for which he pays. Despite a lot of attention data trading has received, a \emph{desirable} mechanism for this scenario is still missing. This is due to the fact that general solutions are inefficient when the size of datasets is considerable or the evaluated function is complicated, and that existing efficient cryptographic techniques cannot fully capture the features of our scenario or can only address very limited computing tasks. In this paper, we propose the \emph{first desirable} mechanism that is practical and supports a wide variety of computing tasks --- evaluation of arbitrary functions that can be represented as polynomials. We introduce a new cryptographic notion called \emph{blind polynomial evaluation} and instantiate it with an explicit protocol. We further combine this notion with the blockchain paradigm to provide a \emph{practical} framework that can satisfy the requirements mentioned above.