Information Leakage Analysis of Inner-Product Functional Encryption Based Data Classification

Abstract

In this work, we study the practical security of innerproduct functional encryption. We left behind the mathematical security proof of the schemes, provided in the literature, and focus on what attackers can use in realistic scenarios without tricking the protocol, and how they can retrieve more than they should be able to. This study is based on the proposed protocol from [1]. We generalize the scenario to an attacker possessing n secret keys. We propose attacks based on machine learning, and experiment them over the MNIST dataset [2].