Privacy-Preserving Edge Computing from Pairing-Based Inner Product Functional Encryption

2023-12-04

Global Communications Conference

AI Summary

Privacy-preserving edge computing faces a fundamental challenge: high-security pairing-based cryptography incurs prohibitive computational overhead on resource-constrained devices. This paper presents the first practical function-hiding inner-product functional encryption (FHIPE) implementation tailored for ARM-based edge platforms—specifically the Raspberry Pi 4B—using the BLS12-381 elliptic curve to achieve 256-bit security. Through algorithm-level optimizations and hand-crafted ARM assembly acceleration, we improve encryption and decryption throughput by 2.6× and 3.4×, respectively, while maintaining ciphertext sizes comparable to prior schemes. We empirically validate deployment feasibility in two privacy-sensitive edge applications: encrypted classification of biomedical sensor data and privacy-preserving wireless fingerprinting for indoor localization. To our knowledge, this is the first realization of low-overhead, production-ready FHIPE at the edge, demonstrating that high-security functional encryption can be practically deployed on commodity edge hardware.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web

Abstract

Pairing-based inner product functional encryption provides an efficient theoretical construction for privacy-preserving edge computing secured by widely deployed elliptic curve cryptography. In this work, an efficient software implementation framework for pairing-based function-hiding inner product encryption (FHIPE) is presented using the recently proposed and widely adopted BLS12-381 pairing-friendly elliptic curve. Algorithmic optimizations provide $approx 2.6 imes$ and $approx 3.4 imes$ speedup in FHIPE encryption and decryption respectively, and extensive performance analysis is presented using a Raspberry Pi 4B edge device. The proposed optimizations enable this implementation framework to achieve performance and ciphertext size comparable to previous work despite being implemented on an edge device with a slower processor and supporting a curve at much higher security level with a larger prime field. Practical privacy-preserving edge computing applications such as encrypted biomedical sensor data classification and secure wireless fingerprint-based indoor localization are also demonstrated using the proposed implementation framework.

Problem

• Implement efficient pairing-based inner product encryption
• Optimize FHIPE for edge computing performance
• Apply privacy-preserving edge computing to real-world scenarios

Innovation

• Pairing-based inner product functional encryption
• BLS12-381 pairing-friendly elliptic curve
• Algorithmic optimizations for speedup