A review of compressive sensing in information security field

The applications of compressive sensing (CS) in the fi eld of information security have captured a great deal of researchers\u27 attention in the past decade. To supply guidance for researchers from a comprehensive perspective, this paper, for the fi rst time, reviews CS in information security field from two aspects: theoretical security and application security. Moreover, the CS applied in image cipher is one of the most widespread applications, as its characteristics of dimensional reduction and random projection can be utilized and integrated into image cryptosystems, which can achieve simultaneous compression and encryption of an image or multiple images. With respect to this application, the basic framework designs and the corresponding analyses are investigated. Speci fically, the investigation proceeds from three aspects, namely, image ciphers based on chaos and CS, image ciphers based on optics and CS, and image ciphers based on chaos, optics, and CS. A total of six frameworks are put forward. Meanwhile, their analyses in terms of security, advantages, disadvantages, and so on are presented. At last, we attempt to indicate some other possible application research topics in future

IoT-Based Multi-Dimensional Chaos Mapping System for Secure and Fast Transmission of Visual Data in Smart Cities

A “smart city” sends data from many sensors to a cloud server for local authorities and the public to connect. Smart city residents communicate mostly through images and videos. Many image security algorithms have been proposed to improve locals’ lives, but a high-class redundancy method with a small space requirement is still needed to acquire and protect this sensitive data. This paper proposes an IoT-based multi-dimensional chaos mapping system for secure and fast transmission of visual data in smart cities, which uses the five dimensional Gauss Sine Logistic system to generate hyper-chaotic sequences to encrypt images. The proposed method also uses pixel position permutation and Singular Value Decomposition with Discrete fractional cosine transform to compress and protect the sensitive image data. To increase security, we use a chaotic system to construct the chaotic sequences and a diffusion matrix. Furthermore, numerical simulation results and theoretical evaluations validate the suggested scheme’s security and efficacy after compression encryption.publishedVersio

Secure Wireless Communications Based on Compressive Sensing: A Survey

IEEE Compressive sensing (CS) has become a popular signal processing technique and has extensive applications in numerous fields such as wireless communications, image processing, magnetic resonance imaging, remote sensing imaging, and anology to information conversion, since it can realize simultaneous sampling and compression. In the information security field, secure CS has received much attention due to the fact that CS can be regarded as a cryptosystem to attain simultaneous sampling, compression and encryption when maintaining the secret measurement matrix. Considering that there are increasing works focusing on secure wireless communications based on CS in recent years, we produce a detailed review for the state-of-the-art in this paper. To be specific, the survey proceeds with two phases. The first phase reviews the security aspects of CS according to different types of random measurement matrices such as Gaussian matrix, circulant matrix, and other special random matrices, which establishes theoretical foundations for applications in secure wireless communications. The second phase reviews the applications of secure CS depending on communication scenarios such as wireless wiretap channel, wireless sensor network, internet of things, crowdsensing, smart grid, and wireless body area networks. Finally, some concluding remarks are given

Visual Privacy Protection Methods: A Survey

Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambient-assisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual’s privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users’ acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people’s privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.This work has been partially supported by the Spanish Ministry of Science and Innovation under project “Sistema de visión para la monitorización de la actividad de la vida diaria en el hogar” (TIN2010-20510-C04-02) and by the European Commission under project “caring4U - A study on people activity in private spaces: towards a multisensor network that meets privacy requirements” (PIEF-GA-2010-274649). José Ramón Padilla López and Alexandros Andre Chaaraoui acknowledge financial support by the Conselleria d'Educació, Formació i Ocupació of the Generalitat Valenciana (fellowship ACIF/2012/064 and ACIF/2011/160 respectively)

Hash Based Four Level Image Cryptography

he paper presents a four level image encryption cryptography based on hash i.e. a replacing table for giving new values to the pixels. The basic motive of this work is to provide a technique for securing the images to the level that one is not able to recognize it while transmission to prevent the attack of intruders. In this paper multi level image cryptography is used base d on chaotic system which employs random integer function for the diffusion phase. The proposed algorithm provides large key space. Results are compared in terms of correlation coefficient which satisfies the property of zero correlation. In this paper it is proposed that multi level image cryptography to securely encrypt the images for the purpose of storing images and transmitting them ov er the Internet. There are two major advantages associated with this system. The first advantage is that it makes the encrypted im age with a constant increasing intensity. The second advantage is that it does not impose any restriction on the decoding of the specifi c image signal because with every new image signal it produces a new hash accordingly. Our system would be systematically evaluated, and it shows a high level of security with excellent image quality

Multi-Level Reversible Data Anonymization via Compressive Sensing and Data Hiding

Recent advances in intelligent surveillance systems have enabled a new era of smart monitoring in a wide range of applications from health monitoring to homeland security. However, this boom in data gathering, analyzing and sharing brings in also significant privacy concerns. We propose a Compressive Sensing (CS) based data encryption that is capable of both obfuscating selected sensitive parts of documents and compressively sampling, hence encrypting both sensitive and non-sensitive parts of the document. The scheme uses a data hiding technique on CS-encrypted signal to preserve the one-time use obfuscation matrix. The proposed privacy-preserving approach offers a low-cost multi-tier encryption system that provides different levels of reconstruction quality for different classes of users, e.g., semi-authorized, full-authorized. As a case study, we develop a secure video surveillance system and analyze its performance.publishedVersionPeer reviewe

ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness