Efficient identity security authentication method based on improved R-LWE algorithm in IoT environment

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

EURASIP Journal on Information Security Pub Date : 2024-02-22 DOI:10.1186/s13635-024-00153-8

Lin Yang

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引用次数: 0

Abstract

In recent years, various smart devices based on IoT technology, such as smart homes, healthcare, detection, and logistics systems, have emerged. However, as the number of IoT-connected devices increases, securing the IoT is becoming increasingly challenging. To tackle the increasing security challenges caused by the proliferation of IoT devices, this research proposes an innovative method for IoT identity authentication. The method is based on an improved ring-learning with errors (R-LWE) algorithm, which encrypts and decrypts communication between devices and servers effectively using polynomial modular multiplication and modular addition operations. The main innovation of this study is the improvement of the traditional R-LWE algorithm, enhancing its efficiency and security. Experimental results demonstrated that, when compared to number theory-based algorithms and elliptic curve cryptography algorithms at a 256-bit security level, the enhanced algorithm achieves significant advantages. The improved algorithm encrypted 20 data points with an average runtime of only 3.6 ms, compared to 7.3 ms and 7.7 ms for the other algorithms. Similarly, decrypting the same amount of data had an average runtime of 2.9 ms, as opposed to 7.3 ms and 8 ms for the other algorithms. Additionally, the improved R-LWE algorithm had significant advantages in terms of communication and storage costs. Compared to the number theory-based algorithm, the R-LWE algorithm reduced communication and storage costs by 3 °C each, and compared to elliptic curve cryptography, it reduced them by 4 °C each. This achievement not only enhances the efficiency of encryption and decryption but also lowers the overall operational costs of the algorithm. The research has made significant strides in improving the security and efficiency of IoT device identity authentication by enhancing the R-LWE algorithm. This study provides theoretical and practical foundations for the development and application of related technologies, as well as new solutions for IoT security.

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物联网环境中基于改进的 R-LWE 算法的高效身份安全认证方法

近年来，基于物联网技术的各种智能设备不断涌现，如智能家居、医疗保健、检测和物流系统等。然而，随着物联网连接设备数量的增加，确保物联网安全也变得越来越具有挑战性。为了应对物联网设备激增带来的日益严峻的安全挑战，本研究提出了一种创新的物联网身份验证方法。该方法基于改进的带误差环学习（R-LWE）算法，利用多项式模块乘法和模块加法运算有效地加密和解密设备与服务器之间的通信。这项研究的主要创新点在于改进了传统的 R-LWE 算法，提高了其效率和安全性。实验结果表明，与基于数论的算法和 256 位安全级别的椭圆曲线加密算法相比，改进算法具有显著优势。改进算法加密 20 个数据点的平均运行时间仅为 3.6 毫秒，而其他算法分别为 7.3 毫秒和 7.7 毫秒。同样，解密相同数量数据的平均运行时间为 2.9 毫秒，而其他算法分别为 7.3 毫秒和 8 毫秒。此外，改进的 R-LWE 算法在通信和存储成本方面也有显著优势。与基于数论的算法相比，R-LWE 算法的通信和存储成本各降低了 3 ℃，而与椭圆曲线加密法相比，则各降低了 4 ℃。这一成果不仅提高了加密和解密的效率，还降低了算法的总体运行成本。该研究通过增强 R-LWE 算法，在提高物联网设备身份验证的安全性和效率方面取得了重大进展。这项研究为相关技术的开发和应用提供了理论和实践基础，也为物联网安全提供了新的解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EURASIP Journal on Information Security COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

8.80

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： The overall goal of the EURASIP Journal on Information Security, sponsored by the European Association for Signal Processing (EURASIP), is to bring together researchers and practitioners dealing with the general field of information security, with a particular emphasis on the use of signal processing tools in adversarial environments. As such, it addresses all works whereby security is achieved through a combination of techniques from cryptography, computer security, machine learning and multimedia signal processing. Application domains lie, for example, in secure storage, retrieval and tracking of multimedia data, secure outsourcing of computations, forgery detection of multimedia data, or secure use of biometrics. The journal also welcomes survey papers that give the reader a gentle introduction to one of the topics covered as well as papers that report large-scale experimental evaluations of existing techniques. Pure cryptographic papers are outside the scope of the journal. Topics relevant to the journal include, but are not limited to: • Multimedia security primitives (such digital watermarking, perceptual hashing, multimedia authentictaion) • Steganography and Steganalysis • Fingerprinting and traitor tracing • Joint signal processing and encryption, signal processing in the encrypted domain, applied cryptography • Biometrics (fusion, multimodal biometrics, protocols, security issues) • Digital forensics • Multimedia signal processing approaches tailored towards adversarial environments • Machine learning in adversarial environments • Digital Rights Management • Network security (such as physical layer security, intrusion detection) • Hardware security, Physical Unclonable Functions • Privacy-Enhancing Technologies for multimedia data • Private data analysis, security in outsourced computations, cloud privacy