Open AccessSpeed Reading in the Dark: Accelerating Functional Encryption for Quadratic Functions with Reprogrammable Hardware
Author(s) -
Milad Bahadori,
Kimmo Järvinen,
Tilen Marc,
Miha Stopar
Publication year - 2021
Publication title -
iacr transactions on cryptographic hardware and embedded systems
Language(s) - English
Resource type - Journals
ISSN - 2569-2925
DOI - 10.46586/tches.v2021.i3.1-27
Subject(s) - mnist database , encryption , computer science , plaintext , benchmark (surveying) , software , functional encryption , computer hardware , embedded system , theoretical computer science , artificial intelligence , ciphertext , operating system , deep learning , geodesy , geography
Functional encryption is a new paradigm for encryption where decryption does not give the entire plaintext but only some function of it. Functional encryption has great potential in privacy-enhancing technologies but suffers from excessive computational overheads. We introduce the first hardware accelerator that supports functional encryption for quadratic functions. Our accelerator is implemented on a reprogrammable system-on-chip following the hardware/software codesign methogology. We benchmark our implementation for two privacy-preserving machine learning applications: (1) classification of handwritten digits from the MNIST database and (2) classification of clothes images from the Fashion MNIST database. In both cases, classification is performed with encrypted images. We show that our implementation offers speedups of over 200 times compared to a published software implementation and permits applications which are unfeasible with software-only solutions.