Aiman Jan

Bio: Aiman Jan is an academic researcher from University of Kashmir. The author has contributed to research in topics: Encryption & Computer science. The author has an hindex of 3, co-authored 5 publications receiving 9 citations.

A Novel Laplacian of Gaussian (LoG) and Chaotic Encryption Based Image Steganography Technique

Abstract: Information sharing through internet has becoming challenge due to high-risk factor of attacks to the information being transferred. In this paper, a novel image-encryption edge based Image steganography technique is proposed. The proposed algorithm uses logistic map for encrypting the information prior to transmission. Laplacian of Gaussian (LoG) edge operator is used to find edge areas of the colored-cover-image. Simulation analysis demonstrates that the proposed algorithm has a good amount of payload along with better results of security analysis. The proposed scheme is compared with the existing-methods.

IEFHAC: Image encryption framework based on hessenberg transform and chaotic theory for smart health

Abstract: Smart cities aim to improve the quality of life by utilizing technological advancements. One of the main areas of innovation includes the design, implementation, and management of data-intensive medical systems also known as big-data Smart Healthcare systems. Smart health systems need to be supported by highly efficient and resilient security frameworks. One of the important aspects that smart health systems need to provide, is timely access to high-resolution medical images, that form about 80% of the medical data. These images contain sensitive information about the patient and as such need to be secured completely. To prevent unauthorized access to medical images, the process of image encryption has become an imperative task for researchers all over the world. Chaos-based encryption has paved the way for the protection of sensitive data from being altered, modified, or hacked. In this paper, we present an Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC), for improving security and reducing computational time while encrypting patient data. IEFHAC uses two 1D-chaotic maps: Logistic map and Sine map for the confusion of data, while diffusion has been achieved by applying the Hessenberg household transform. The Sin and Logistic maps are used to regeneratively affect each other's output, as such dynamically changing the key parameters. The experimental analysis demonstrates that IEFHAC shows better results like NPCR ranging from 99.66 to 100%, UACI of 37.39%, lesser computational time of 0.36 s, and is more robust to statistical attacks.

Logistic Map-Based Image Steganography Using Edge Detection

Abstract: Transferring data via an insecure network has become a challenge among researchers in this fast-growing technological world. In this paper, a double-layer security framework based on cryptography and steganography has been developed and tested. Logistic maps have been used for encrypting data before embedding it into cover images. The encrypted data has been hidden into edge areas of the cover images to ensure better imperceptivity at a given payload. The scheme has been evaluated in terms of various objective parameters like peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity index (SSIM). Besides, the strength of the cryptographic algorithm based on logistic map using number of changes per rate (NPCR), unified average changed intensity (UACI), and entropy has been computed. Our framework reports the average PSNR value of 44.61 dB for payload of around 1.72 bits per pixel (bpp). In addition, the NPCR value of about 100%, UACI value of 36.72, and entropy value of 7.96 depict that our scheme is capable of providing ample security to the data to be transmitted.

Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms

Abstract: Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.