Kryptein: A Compressive-Sensing-Based Encryption Scheme for the Internet of Things

被引:23
|
作者
Xue, Wanli [1 ,2 ]
Luo, Chengwen [3 ]
Lan, Guohao [1 ,2 ]
Rana, Rajib [4 ]
Hu, Wen [1 ,2 ]
Seneviratne, Aruna [1 ,2 ]
机构
[1] UNSW, Sydney, NSW, Australia
[2] CSIRO, Data61, Canberra, ACT, Australia
[3] Shenzhen Univ, Shenzhen, Peoples R China
[4] Univ Southern Queensland, Toowoomba, Qld, Australia
关键词
Compressive Sensing; Security; Encryption; Internet of Things; PRIVACY;
D O I
10.1145/3055031.3055079
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Internet of Things (IoT) is flourishing and has penetrated deeply into people's daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the cloud. The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the cloud. In this work, we propose Kryptein, a compressive-sensing-based encryption scheme for cloud-enabled IoT systems to secure the interaction between the IoT devices and the cloud. Kryptein supports random compressed encryption, statistical decryption, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption. The performance of Kryptein is also measured on off-the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices.
引用
收藏
页码:169 / 180
页数:12
相关论文
共 50 条
  • [21] Subdata image encryption scheme based on compressive sensing and vector quantization
    Haiju Fan
    Kanglei Zhou
    En Zhang
    Wenying Wen
    Ming Li
    Neural Computing and Applications, 2020, 32 : 12771 - 12787
  • [22] Image Compression and Encryption Scheme Based on Compressive Sensing and Fourier Transform
    Zhang, Miao
    Tong, Xiao-Jun
    Liu, Jie
    Wang, Zhu
    Liu, Jinlong
    Liu, Baolong
    Ma, Jing
    IEEE ACCESS, 2020, 8 : 40838 - 40849
  • [23] Scalable Compressive Sensing-Based Multi-User Detection Scheme for Internet-of-Things Applications
    Liu, Jiachen
    Cheng, Hung-Yi
    Liao, Ching-Chun
    Wu, An-Yeu
    2015 IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING SYSTEMS (SIPS 2015), 2015,
  • [24] A Compressive-Sensing-Based Approach for the Detection and Characterization of Buried Objects
    Ambrosanio, Michele
    Pascazio, Vito
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2015, 8 (07) : 3386 - 3395
  • [25] Stabilized and Fast Method for Compressive-Sensing-Based Method of Moments
    Gao, Yalan
    Akbar, Muhammad Firdaus
    Jawad, Ghassan Nihad
    IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, 2023, 22 (12): : 2915 - 2919
  • [26] Color image compression and encryption scheme based on compressive sensing and double random encryption strategy
    Chai, Xiuli
    Bi, Jianqiang
    Gan, Zhihua
    Liu, Xianxing
    Zhang, Yushu
    Chen, Yiran
    SIGNAL PROCESSING, 2020, 176
  • [27] Anonymous Aggregate Encryption Scheme for Industrial Internet of Things
    Deng, Lunzhi
    IEEE SYSTEMS JOURNAL, 2020, 14 (03): : 3999 - 4006
  • [28] A Blockchain-Based Secure Image Encryption Scheme for the Industrial Internet of Things
    Khan, Prince Waqas
    Byun, Yungcheol
    ENTROPY, 2020, 22 (02)
  • [29] Efficient Identity-Based Broadcast Encryption Scheme on Lattices for the Internet of Things
    He, Kai
    Liu, Xueqiao
    Liu, Jia-Nan
    Liu, Wei
    SECURITY AND COMMUNICATION NETWORKS, 2021, 2021
  • [30] A Lightweight Attribute Based Encryption Scheme with Constant Size Ciphertext for Internet of Things
    Yang, Wenti
    Wang, Ruimiao
    Guan, Zhitao
    Wu, Longfei
    Du, Xiaojiang
    Guizani, Mohsen
    ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2020,