Efficient Homomorphic Encryption for Multikey Compressed Sensing in Lightweight Cloud-Based Image Processing

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yuning Qi;Jingguo Bi;Haipeng Peng;Lixiang Li
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引用次数: 0

Abstract

With the rapid development of cloud storage and privacy computing technologies, users with limited resources are increasingly relying on cloud servers for the storage and computation of their image data. This approach ensures data security and convenient access. However, current image data security-sharing schemes that support privacy computing often face issues such as high bandwidth consumption and significant ciphertext expansion, limiting their applicability. To address these challenges, we propose an innovative multikey compressed sensing lightweight encryption scheme (MCSLE), based on compressed sensing (CS) technology. This scheme is the first to design a multikey conversion algorithm for CS. It allows each sampling end to independently compress the sampled images using different keys. The cloud platform then completes the key conversion and unification. It provides flexible compression sampling capabilities, a robust privacy protection mechanism, and comprehensive support for homomorphic computing. Furthermore, we have designed a specialized image reconstruction algorithm for this scheme. It has undergone in-depth testing in various practical application scenarios, including medical image analysis, fire monitoring, and handwritten text recognition. The experimental results demonstrate that, unlike existing schemes with several tens of times ciphertext expansion, MCSLE can support homomorphic computation at a compression rate of 0.5 while maintaining high-quality image reconstruction.
轻量级云图像处理中多密钥压缩感知的高效同态加密
随着云存储和隐私计算技术的快速发展,资源有限的用户越来越依赖云服务器来存储和计算图像数据。这种方法既能保证数据安全,又能方便访问。然而,目前支持隐私计算的图像数据安全共享方案往往面临着高带宽消耗和大量密文扩展等问题,限制了其适用性。为了应对这些挑战,我们提出了一种基于压缩传感(CS)技术的创新型多密钥压缩传感轻量级加密方案(MCSLE)。该方案首次为 CS 设计了多密钥转换算法。它允许每个采样端使用不同的密钥独立压缩采样图像。然后由云平台完成密钥转换和统一。它提供了灵活的压缩采样能力、强大的隐私保护机制以及对同态计算的全面支持。此外,我们还为该方案设计了专门的图像重建算法。它在医疗图像分析、火灾监控和手写文本识别等各种实际应用场景中进行了深入测试。实验结果表明,与密文扩展几十倍的现有方案不同,MCSLE 可以在 0.5 的压缩率下支持同态计算,同时保持高质量的图像重建。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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