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

• URL: http://arxiv.org/abs/2504.02068v1
• Date: Wed, 02 Apr 2025 19:01:10 GMT
• Title: Privacy-Preserving Edge Computing from Pairing-Based Inner Product Functional Encryption
• Authors: Utsav Banerjee,
• Abstract summary: This work presents an efficient software implementation framework for pairing-based function-hiding inner product encryption (FHIPE)<n> Algorithmic optimizations provide $approx 2.6 times$ and $approx 3.4 times$ speedup in FHIPE encryption and decryption respectively.<n> Practical privacy-preserving edge computing applications such as encrypted biomedical sensor data classification and secure wireless fingerprint-based indoor localization are also demonstrated.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• MST3 Encryption improvement with three-parameter group of Hermitian function field [0.0]
  This work presents an advanced cryptographic framework utilizing automorphism groups as the foundational structure for encryption scheme implementation. A key innovation in this implementation is utilizing the Hermitian function field as the underlying mathematical framework. The encryption mechanism features phased key de-encapsulation from ciphertext, representing a substantial advantage over alternative implementations.
  arXiv  Detail & Related papers  (2025-04-21T18:58:42Z)
• CIPHERMATCH: Accelerating Homomorphic Encryption-Based String Matching via Memory-Efficient Data Packing and In-Flash Processing [8.114331115730021]
  Homomorphic encryption (HE) allows secure computation on encrypted data without revealing the original data.<n>Many cloud computing applications (e.g., DNA read mapping, biometric matching, web search) use exact string matching as a key operation.<n>Prior string matching algorithms that use homomorphic encryption are limited by high computational latency.
  arXiv  Detail & Related papers  (2025-03-12T00:25:58Z)
• Accelerating Private Large Transformers Inference through Fine-grained Collaborative Computation [8.859237832459876]
  We present FASTLMPI, a new approach to accelerate private TBM inference through fine-grained optimization.<n>Specifically, through the fine-grained co-design of homomorphic encryption and secret sharing, FASTLMPI achieves efficient protocols for matrix multiplication, SoftMax, LayerNorm, and GeLULU.<n>FASTLMPI shows a remarkable 54% to 64% decrease in runtime and an impressive 72.2% reduction in communication costs.
  arXiv  Detail & Related papers  (2024-12-21T08:33:12Z)
• Qubit Optimized Quantum Implementation of SLIM [0.0]
  We introduce a novel quantum implementation of SLIM, a lightweight block cipher optimized for 32-bit plaintext and an 80-bit key. This work highlights SLIM's potential as a resource-efficient and secure candidate for quantum-resistant encryption protocols.
  arXiv  Detail & Related papers  (2024-12-14T13:52:36Z)
• Encrypted system identification as-a-service via reliable encrypted matrix inversion [0.0]
  Encrypted computation opens up promising avenues across a plethora of application domains. In particular, Arithmetic homomorphic encryption is a natural fit for cloud-based computational services. This paper presents an encrypted system identification service enabled by a reliable encrypted solution to at least squares problems.
  arXiv  Detail & Related papers  (2024-10-27T20:00:04Z)
• Encryption-Friendly LLM Architecture [11.386436468650016]
  Homomorphic encryption (HE) is a cryptographic protocol supporting arithmetic computations in encrypted states.<n>We propose a modified HE-friendly transformer architecture with an emphasis on inference following personalized (private) fine-tuning.
  arXiv  Detail & Related papers  (2024-10-03T13:48:35Z)
• SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
  We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
  arXiv  Detail & Related papers  (2023-09-27T05:19:32Z)
• Theoretically Principled Federated Learning for Balancing Privacy and Utility [61.03993520243198]
  We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters. It can achieve personalized utility-privacy trade-off for each model parameter, on each client, at each communication round in federated learning.
  arXiv  Detail & Related papers  (2023-05-24T13:44:02Z)
• THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption [112.02441503951297]
  Privacy-preserving inference of transformer models is on the demand of cloud service users. We introduce $textitTHE-X$, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models.
  arXiv  Detail & Related papers  (2022-06-01T03:49:18Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Differentially Private Federated Bayesian Optimization with Distributed Exploration [48.9049546219643]
  We introduce differential privacy (DP) into the training of deep neural networks through a general framework for adding DP to iterative algorithms. We show that DP-FTS-DE achieves high utility (competitive performance) with a strong privacy guarantee. We also use real-world experiments to show that DP-FTS-DE induces a trade-off between privacy and utility.
  arXiv  Detail & Related papers  (2021-10-27T04:11:06Z)
• A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework [56.57225686288006]
  Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices. Previous pruning methods mainly focus on reducing the model size and/or improving performance without considering the privacy of user data. We propose a privacy-preserving-oriented pruning and mobile acceleration framework that does not require the private training dataset.
  arXiv  Detail & Related papers  (2020-03-13T23:52:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.