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Fast and secured cloud assisted recovery scheme for compressively sensed signals using new chaotic system

机译:使用新型混沌系统的快速安全的云辅助恢复方案,用于压缩感测信号

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摘要

With recent advancement in Sensors technology, multimedia data has been exponentially generated every day. As a result, there is always a huge demand for fast data processing and storage. In order to effectively acquire and process such a huge amount of data, the concept of compressive sensing (CS) as well as the abundant computing and storage resources of cloud have been increasingly used nowadays. In this paper, we propose a novel secured cloud assisted recovery scheme for compressively sensed signals using a proposed new chaotic map that has wider chaotic range and better attributes when compared to existing maps. With pseudo randomness and unpredictability of the chaotic sequence, generated using the proposed chaotic map, sensing matrix for CS problem and the encryption algorithm are designed. The proposed system ensures that the data owners securely outsource the compressively sensed samples to cloud which occupies less storage. Data users then insist cloud to perform the complex reconstruction problem in an encrypted domain with substantial computational cost being shifted to cloud. Cloud performs the expensive reconstruction problem and provides the reconstructed signal in encrypted form which is later decrypted by the data users. Cloud thus gets no knowledge about the original underlying data samples ensuring privacy of the proposed system. Empirical analysis on the proposed system shows satisfactory compression and security performance on both one dimensional and two dimensional data. The simulation results prove the efficiency of the proposed cloud assisted scheme.
机译:随着传感器技术的最新发展,多媒体数据每天都以指数方式生成。结果,总是对快速数据处理和存储有巨大的需求。为了有效地获取和处理如此大量的数据,如今越来越多地使用压缩感测(CS)的概念以及云的丰富计算和存储资源。在本文中,我们提出了一种新颖的安全云辅助恢复方案,该方案使用拟议的新混沌图来对压缩感测信号进行处理,与现有图相比,该混沌图具有更宽的混沌范围和更好的属性。利用所提出的混沌映射图生成的伪随机性和混沌序列的不可预测性,设计了CS问题的感知矩阵和加密算法。提出的系统可确保数据所有者将压缩感知的样本安全地外包给占用较少存储空间的云。然后,数据用户坚持认为云将在加密域中执行复杂的重建问题,而大量的计算成本将转移到云中。云执行昂贵的重建问题,并以加密形式提供重建的信号,然后由数据用户解密。因此,云不了解原始底层数据样本,从而无法确保所提议系统的私密性。对提出的系统进行的经验分析表明,在一维和二维数据上都具有令人满意的压缩和安全性能。仿真结果证明了所提出的云辅助方案的有效性。

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