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融合DES和ECC算法的物联网隐私数据加密方法

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为避免物联网隐私数据在加密过程中产生较多重复数据，导致计算复杂度较高，降低计算效率和安全性问题，提出融合DES(Data Encryption Standard)和ECC(Ellipse Curve Ctyptography)算法的物联网隐私数据加密方法。首先，采用TF-IDF(Tem Frequency-Inverse Document Frequency)算法提取物联网隐私数据中的特征向量，输入 BP(Back Propagation)神经网络中并进行训练，利用 IQPSO(Improved Quantum Particle Swarm Optimization)算法优化神经网络，完成对物联网隐私数据中重复数据的去除处理;其次，分别利用DES算法和ECC算法对物联网隐私数据实施一、二次加密;最后，采取融合DES和ECC算法进行数字签名加密，实现对物联网隐私数据的完整加密。实验结果表明，该算法具有较高的计算效率、安全性以及可靠性。
Encryption Method of Privacy Data for Internet of Things Based on Fusion of DES and ECC Algorithms
In order to avoid more duplicate data in the encryption process of IoT privacy data,which leads to higher computational complexity and reduces computational efficiency and security,an encryption method of IoT privacy data that combines DES(Data Encryption Standard)and ECC(Ellipse Curve Ctyptography)algorithms is proposed.Firstly,the TF-IDF(Tem Frequency-Inverse Document Frequency)algorithm is used to extract feature vectors from the privacy data of the Internet of Things.They are input into the BP(Back Proragation)neural network and are trained.The IQPSO(Improved Quantum Particle Swarm Optimization)algorithm is used to optimize the neural network and complete the removal of duplicate data from the privacy data of the Internet of Things.Secondly,the Data Encryption Standard and ECC algorithm are used to implement the primary and secondary encryption of the privacy data of the Internet of Things.Finally,a fusion of DES and ECC algorithms is adopted for digital signature encryption to achieve complete encryption of IoT privacy data.The experimental results show that the proposed algorithm has high computational efficiency,security,and reliability.

data encryption standardellipse curve cryptography(ECC)internet of things data encryptionterm frequency-inverse document frequency(TF-IDF)improved quantum particle swarm optimizationI(QPSO)digital signature

唐锴令、郑皓

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长沙矿冶研究院海洋矿产资源开发利用技术研究所,长沙 410012

DES算法 ECC算法 物联网数据加密 TF-IDF算法 IQPSO算法 数字签名

湖南省自然科学基金资助项目

2022JK60058

2024

吉林大学学报(信息科学版)
吉林大学

吉林大学学报(信息科学版)

CSTPCD
影响因子：0.607
ISSN：1671-5896
年,卷(期)：2024.42(3)
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