Biblio
Existing secure deletion approaches are inefficient in erasing data permanently because file systems have no knowledge of the data layout on the storage device, nor is the storage device aware of file information within the file systems. This inefficiency is exaggerated on the emerging shingled magnetic recording (SMR) drive due to its inherent sequential-write constraint. On SMR drives, secure deletion requests may lead to serious write amplification and performance degradation if the data layout is not properly configured. Such observation motivates us to propose a file-oriented fast secure deletion (FFSD) strategy to alleviate the negative impacts of SMR drives' sequential-write constraint and improve the efficiency of secure deletion operations on SMR drives. A series of experiments was conducted to demonstrate the capability of the proposed strategy on improving the efficiency of secure deletion on SMR drives.
Because cloud storage services have been broadly used in enterprises for online sharing and collaboration, sensitive information in images or documents may be easily leaked outside the trust enterprise on-premises due to such cloud services. Existing solutions to this problem have not fully explored the tradeoffs among application performance, service scalability, and user data privacy. Therefore, we propose CloudDLP, a generic approach for enterprises to automatically sanitize sensitive data in images and documents in browser-based cloud storage. To the best of our knowledge, CloudDLP is the first system that automatically and transparently detects and sanitizes both sensitive images and textual documents without compromising user experience or application functionality on browser-based cloud storage. To prevent sensitive information escaping from on-premises, CloudDLP utilizes deep learning methods to detect sensitive information in both images and textual documents. We have evaluated the proposed method on a number of typical cloud applications. Our experimental results show that it can achieve transparent and automatic data sanitization on the cloud storage services with relatively low overheads, while preserving most application functionalities.
In this paper, new image encryption based on singular value decomposition (SVD), fractional discrete cosine transform (FrDCT) and the chaotic system is proposed for the security of medical image. Reliability, vitality, and efficacy of medical image encryption are strengthened by it. The proposed method discusses the benefits of FrDCT over fractional Fourier transform. The key sensitivity of the proposed algorithm for different medical images inspires us to make a platform for other researchers. Theoretical and statistical tests are carried out demonstrating the high-level security of the proposed algorithm.
Signal processing in encrypted domain has become an important mean to protect privacy in an untrusted network environment. Due to the limitations of the underlying encryption methods, many useful algorithms that are sophisticated are not well implemented. Considering that QR decomposition is widely used in many fields, in this paper, we propose to implement QR decomposition in homomorphic encrypted domain. We firstly realize some necessary primitive operations in homomorphic encrypted domain, including division and open square operation. Gram-Schmidt process is then studied in the encrypted domain. We propose the implementation of QR decomposition in the encrypted domain by using the secure implementation of Gram-Schmidt process. We conduct experiments to demonstrate the effectiveness and analyze the performance of the proposed outsourced QR decomposition.
Accurate network traffic identification is an important basis for network traffic monitoring and data analysis, and is the key to improve the quality of user service. In this paper, through the analysis of two network traffic identification methods based on machine learning and deep packet inspection, a network traffic identification method based on machine learning and deep packet inspection is proposed. This method uses deep packet inspection technology to identify most network traffic, reduces the workload that needs to be identified by machine learning method, and deep packet inspection can identify specific application traffic, and improves the accuracy of identification. Machine learning method is used to assist in identifying network traffic with encryption and unknown features, which makes up for the disadvantage of deep packet inspection that can not identify new applications and encrypted traffic. Experiments show that this method can improve the identification rate of network traffic.
Security of data in the Internet of Things (IoT) deals with Encryption to provide a stable secure system. The IoT device possess a constrained Main Memory and Secondary Memory that mandates the use of Elliptic Curve Cryptographic (ECC) scheme. The Scalar Multiplication has a great impact on the ECC implementations in reducing the Computation and Space Complexity, thereby enhancing the performance of an IoT System providing high Security and Privacy. The proposed High Speed Split Multiplier (HSSM) for ECC in IoT is a lightweight Multiplication technique that uses Split Multiplication with Pseudo-Mersenne Prime Number and Montgomery Curve to withstand the Power Analysis Attack. The proposed algorithm reduces the Computation Time and the Space Complexity of the Cryptographic operations in terms of Clock cycles and RAM when compared with Liu et al.,’s multiplication algorithms [1].
In this work, we will present a new hybrid cryptography method based on two hard problems: 1- The problem of the discrete logarithm on an elliptic curve defined on a finite local ring. 2- The closest vector problem in lattice and the conjugate problem on square matrices. At first, we will make the exchange of keys to the Diffie-Hellman. The encryption of a message is done with a bad basis of a lattice.
Digital image security is now a severe issue, especially when sending images to telecommunications networks. There are many ways where digital images can be encrypted and decrypted from secure communication. Digital images contain data that is important when captured or disseminated to preserve and preserve data. The technique of encryption is one way of providing data on digital images. A key cipher block chaining and Gingerbreadman Map are used in our search to encrypt images. This new system uses simplicity, high quality, enhanced by the vehicle's natural efficiency and the number of the chain. The proposed method is performed for experimental purposes and the experiments are performed in- depth, highly reliable analysis. The results confirm that by referring to several known attacks, the plan cannot be completed. Comparative studies with other algorithms show a slight rise in the security of passwords with the advantages of security of the chain. The results of this experiment are a comparison of button sensitivity and a comparison after encryption and decryption of the initial image using the amount of pixel change rate and unified average change intensity.
The main objective of this paper is to present a more secured and computationally efficient procedure of encrypting and decrypting images using the enigma algorithm in comparison to the existing methods. Available literature on image encryptions and descriptions are not highly secured in every case.To achieve more secured image processing for highly advanced technologies, a proposed algorithm can be the process used in enigma machine for image encryption and decryption. Enigma machine is piece of spook hardware that was used frequently during the World War II by the Germans. This paper describes the detailed algorithm along with proper demonstration of several essential components present in an enigma machine that is required for image security. Each pixel in a colorful picture can be represented by RGB (Red, Green, Blue) value. The range of RGB values is 0 to 255 that states the red, green and blue intensity of a particular picture.These RGB values are accessed one by one and changed into another by various steps and hence it is not possible to track the original RGB value. In order to retrieve the original image, the receiver needs to know the setting of the enigma. To compare the decrypted image with the original one,these two images are subtracted and their results are also discussed in this paper.
Cloud Computing is the most promising paradigm in recent times. It offers a cost-efficient service to individual and industries. However, outsourcing sensitive data to entrusted Cloud servers presents a brake to Cloud migration. Consequently, improving the security of data access is the most critical task. As an efficient cryptographic technique, Ciphertext Policy Attribute Based Encryption(CP-ABE) develops and implements fine-grained, flexible and scalable access control model. However, existing CP-ABE based approaches suffer from some limitations namely revocation, data owner overhead and computational cost. In this paper, we propose a sliced revocable solution resolving the aforementioned issues abbreviated RS-CPABE. We applied splitting algorithm. We execute symmetric encryption with Advanced Encryption Standard (AES)in large data size and asymmetric encryption with CP-ABE in constant key length. We re-encrypt in case of revocation one single slice. To prove the proposed model, we expose security and performance evaluation.
Today, there are several applications which allow us to share images over the internet. All these images must be stored in a secure manner and should be accessible only to the intended recipients. Hence it is of utmost importance to develop efficient and fast algorithms for encryption of images. This paper uses chaotic generators to generate random sequences which can be used as keys for image encryption. These sequences are seemingly random and have statistical properties. This makes them resistant to analysis and correlation attacks. However, these sequences have fixed cycle lengths. This restricts the number of sequences that can be used as keys. This paper utilises neural networks as a source of perturbation in a chaotic generator and uses its output to encrypt an image. The robustness of the encryption algorithm can be verified using NPCR, UACI, correlation coefficient analysis and information entropy analysis.
This paper presents the encryption of advanced pictures dependent on turmoil hypothesis. Two principal forms are incorporated into this method those are pixel rearranging and pixel substitution. Disorder hypothesis is a part of science concentrating on the conduct of dynamical frameworks that are profoundly touchy to beginning conditions. A little change influences the framework to carry on totally unique, little changes in the beginning position of a disorganized framework have a major effect inevitably. A key of 128-piece length is created utilizing mayhem hypothesis, and decoding should be possible by utilizing a similar key. The bit-XOR activity is executed between the unique picture and disorder succession x is known as pixel substitution. Pixel rearranging contains push savvy rearranging and section astute rearranging gives extra security to pictures. The proposed strategy for encryption gives greater security to pictures.
Image encryption is an essential part of a Visual Cryptography. Existing traditional sequential encryption techniques are infeasible to real-time applications. High-performance reformulations of such methods are increasingly growing over the last decade. These reformulations proved better performances over their sequential counterparts. A rotational encryption scheme encrypts the images in such a way that the decryption is possible with the rotated encrypted images. A parallel rotational encryption technique makes use of a high-performance device. But it less-leverages the optimizations offered by them. We propose a rotational image encryption technique which makes use of memory coalescing provided by the Compute Unified Device Architecture (CUDA). The proposed scheme achieves improved global memory utilization and increased efficiency.
Due to greater network capacity and faster data speed, fifth generation (5G) technology is expected to provide a huge improvement in Internet of Things (IoTs) applications, Augmented & Virtual Reality (AR/VR) technologies, and Machine Type Communications (MTC). Consumer will be able to send/receive high quality multimedia data. For the protection of sensitive multimedia data, a large number of encryption algorithms are available, however, these encryption schemes does not provide light-weight encryption solution for real-time application requirements. This paper proposes a new multi-chaos computational efficient encryption for digital images. In the proposed scheme, plaintext image is transformed using Lifting Wavelet Transform (LWT) and only one-fourth part of the transformed image is encrypted using light-weight Chebyshev and Intertwining maps. Both chaotic maps were chaotically coupled for the confusion and diffusion processes which further enhances the image security. Encryption/decryption speed and other security measures such as correlation coefficient, entropy, Number of Pixels Change Rate (NPCR), contrast, energy, homogeneity confirm the superiority of the proposed light-weight encryption scheme.