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

Utsav Banerjee

TL;DR

The paper tackles the challenge of privacy-preserving edge computing by deploying a pairing-based function-hiding inner product encryption (FHIPE) scheme on the BLS12-381 curve. It introduces an efficient software framework implemented in C using the MIRACL library, featuring GLS-based 4D scalar decomposition, optimized multi-pairing sharing, and a power-tree based bounded discrete log, all demonstrated on a Raspberry Pi 4B. The results show substantial encryption and decryption speedups (≈2.6× and ≈3.4×, respectively) and reduced ciphertext size via twist-based compression, enabling practical privacy-preserving tasks such as encrypted biomedical data classification and secure wireless indoor localization on edge devices. The work lays a path for hardware acceleration and expansion to broader privacy-preserving machine learning tasks in resource-constrained environments, highlighting its applicability to real-world IoT/privacy-sensitive applications.

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 $\approx 2.6 \times$ and $\approx 3.4 \times$ 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.

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

TL;DR

The paper tackles the challenge of privacy-preserving edge computing by deploying a pairing-based function-hiding inner product encryption (FHIPE) scheme on the BLS12-381 curve. It introduces an efficient software framework implemented in C using the MIRACL library, featuring GLS-based 4D scalar decomposition, optimized multi-pairing sharing, and a power-tree based bounded discrete log, all demonstrated on a Raspberry Pi 4B. The results show substantial encryption and decryption speedups (≈2.6× and ≈3.4×, respectively) and reduced ciphertext size via twist-based compression, enabling practical privacy-preserving tasks such as encrypted biomedical data classification and secure wireless indoor localization on edge devices. The work lays a path for hardware acceleration and expansion to broader privacy-preserving machine learning tasks in resource-constrained environments, highlighting its applicability to real-world IoT/privacy-sensitive applications.

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 and 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.

Paper Structure

This paper contains 13 sections, 2 equations, 9 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Elliptic Curves and Pairings
  4. Function-Hiding Inner Product Encryption (FHIPE)
  5. FHIPE Implementation and Optimization
  6. Scalar Multiplication in $\mathbb{G}_2$ for FHIPE Encryption
  7. Multi-Pairing in $\mathbb{G}_1 \times \mathbb{G}_2 \rightarrow \mathbb{G}_{T}$ for FHIPE Decryption
  8. Bounded Discrete Logarithm in $\mathbb{G}_{T}$ for FHIPE Decryption
  9. Overall Computation and Communication Costs
  10. Applications
  11. Privacy-Preserving Biomedical Data Classification
  12. Privacy-Preserving Indoor Localization
  13. Conclusions and Future Work

Figures (9)

  • Figure 1: Structure of 381-bit $\mathbb{F}_p$ element representation in MIRACL software.
  • Figure 2: Computation cost of $n$-fold multi-pairing with various optimizations.
  • Figure 3: Computation of $d_1^2, d_1^3, \cdots, d_1^8$ for $d_1 \in \mathbb{G}_{T}$ with (left) only multiplications and (right) both squarings and multiplications with power tree.
  • Figure 4: Compute cost of FHIPE Encrypt for different vector dimensions ($n$): (left) execution time and (right) memory requirement.
  • Figure 5: Compute cost of FHIPE Decrypt for different vector dimensions ($n$) and table size ($\alpha$): (left) execution time and (right) memory requirement.
  • ...and 4 more figures