Security Analysis of Bit plane Level Image Encryption Schemes

A selective bit-plane encryption scheme was proposed for securing the transmission of image data in mobile environments with a claim that it provides a high security viz. the encryption of the four most significant bit-planes is sufficient for a high image data security. This paper presents the security analysis of the said encryption scheme and reports new important results. We perform the security analysis of the bit-level encryption by considering the normal images and their histogram equalised enhanced images. We consider different bit-plane aspects to analyse the security of the image encryption, and show that the encryption of the four most significant bit-planes is not adequate. The contents of the images can be obtained even when all the bit-planes except one least significant bit-plane are encrypted in the histogram equalised images as shown in the results. The bit-plane level security analysis seems very useful for the analysis of the bit-plane level image encryption schemes

Provably secure and efficient audio compression based on compressive sensing

The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security

Image Security using Visual Cryptography

Informations are being transferred through open channels and the security of those informations has been prime concerns. Apart from many conventional cryptographic schemes, visual cryptographic techniques have also been in use for data and information security. Visual cryptography is a secret sharing scheme as it breaks an original image into image shares such that, when the shares are stacked on one another, a hidden secret image is revealed. The Visual Cryptography Scheme is a secure method that encrypts a secret document or image by breaking it into image shares. A unique property of Visual Cryptography Scheme is that one can visually decode the secret image by superimposing shares without computation. Even to make the visual cryptography image shares more secure, public key encryption scheme is applied. Public key encryption technique makes image shares so secure that it becomes very hard for a third party to decode the secret image information without having required data that is a private key

On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent Gaussian random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this paper, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201