Haleem Farman

A Secure Method for Color Image Steganography using Gray-Level Modification and Multi-level Encryption

Security of information during transmission is a major issue in this modern era. All of the communicating bodies want confidentiality, integrity, and authenticity of their secret information. Researchers have presented various schemes to cope with these Internet security issues. In this context, both steganography and cryptography can be used effectively. However, major limitation in the existing steganographic methods is the low-quality output stego images, which consequently results in the lack of security. To cope with these issues, we present an efficient method for RGB images based on gray level modification (GLM) and multi-level encryption (MLE). The secret key and secret data is encrypted using MLE algorithm before mapping it to the grey-levels of the cover image. Then, a transposition function is applied on cover image prior to data hiding. The usage of transpose, secret key, MLE, and GLM adds four different levels of security to the proposed algorithm, making it very difficult for a malicious user to extract the original secret information. The proposed method is evaluated both quantitatively and qualitatively. The experimental results, compared with several state-of-the-art algorithms, show that the proposed algorithm not only enhances the quality of stego images but also provides multiple levels of security, which can significantly misguide image steganalysis and makes the attack on this algorithm more challenging.

A Novel Image Steganographic Approach for Hiding Text in Color Images using HSI Color Model

2 Abstract: Image Steganography is the process of embedding text in images such that its existence cannot be detected by Human Visual System (HVS) and is known only to sender and receiver. This paper presents a novel approach for image steganography using Hue-Saturation-Intensity (HSI) color space based on Least Significant Bit (LSB). The proposed method transforms the image from RGB color space to Hue-Saturation-Intensity (HSI) color space and then embeds secret data inside the Intensity Plane (I-Plane) and transforms it back to RGB color model after embedding. The said technique is evaluated by both subjective and Objective Analysis. Experimentally it is found that the proposed method have larger Peak Signal-to Noise Ratio (PSNR) values, good imperceptibility and multiple security levels which shows its superiority as compared to several existing methods.

A framework for secure and privacy protected collaborative contents sharing using public OSN

A major issue thoroughly raised and potentially vulnerable in online social networks (OSNs) is the user privacy preservation. Generally, the issue of privacy is tackled from the user point of view without considering the service providers and the third-party data collectors, who can manipulate user data for third party advertisement and behavioral analysis. Therefore, the privacy risk exists not only from the unauthorized users but also from the OSN service providers. To secure data from both the unauthorized users and OSN service providers a framework for collaborative contents sharing is proposed. In the proposed framework, contents are secured at the time of data dissemination and provided assurance about the data sharing among the legitimate users based on collaborative contents sharing. The proposed framework assures not only the confidentiality of contents but also the privacy of data owner and co-owners in OSN. An architecture is provided to support the theoretical and practical basis for the proposed framework; which exhibits the objective of preserving user and contents privacy.

Multi-criteria based zone head selection in Internet of Things based wireless sensor networks

Abstract The past few years have seen dramatic development and a great interest in efficient service delivery and better resource utilization in the Internet of Things (IoT) based constrained Wireless Sensor Network (WSN). The IoT is mainly dependent on optimal deployment of energy aware WSN and efficient communication architecture for data transfer among heterogeneous devices. In addition, energy efficient clustering techniques for WSN node deployment and routing have achieved great involvement for prolonging network lifetime. In clustering technique, where the network is partitioned into different segments (clusters or zones) and proper attention must be given to the cluster head (CH) selection procedure for maximizing node reachability inside the cluster and efficient communication to the base station. In this paper, we have proposed multi-criteria based cluster head/zone head selection scheme in Internet of Things based WSN by considering distinct parameters affecting node energy and network lifetime. These parameters; energy level, distance from neighboring nodes, distance from center of the zone, number of times a node has been zone head and whether a node is merged or not, have direct impact on overall performance of WSN. The relative impact of each parameter in CH/ZH selection is computed using the Analytical Network Process (ANP) which is widely used multi-criteria decision tool. Simulation results of the proposed scheme show relatively better performance than existing energy efficient clustering techniques. The obtained results have been analyzed by varying the number of parameters in ZH selection and their impact on network stability and lifetime.

Multicriteria-Based Location Privacy Preservation in Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) are the preferable choice for Intelligent Transportation Systems (ITS) because of its prevailing significance in both safety and nonsafety applications. Information dissemination in a multihop fashion along with privacy preservation of source node is a serious but challenging issue. We have used the idea of the phantom node as the next forwarder for data dissemination. The phantom node (vehicle) hides the identity of actual source node thus preserving the location privacy. The selection of the phantom node among the set of alternatives’ candidate vehicles is considered as a multicriteria-based problem. The phantom node selection problem is solved by using an analytical network process (ANP) by considering different traffic scenarios. The selection is based on different parameters which are distance, speed, trust, acceleration, and direction. The best alternative (target phantom vehicle) is selected through an ANP where all the alternatives are ranked from best to worst. The vehicle having maximum weight is considered to be the best choice as a phantom node. In order to check the stability of the alternatives’ ranking, sensitivity analysis is performed by taking into account different traffic scenarios and interest level of candidate vehicles.

Clustered genetic semantic graph approach for multi-document abstractive summarization

Multi-document summarization aims to produce a compressed version of numerous online text documents and preserves the salient information. A particular challenge for multi-document summarization is that there is an inevitable overlap in the information stored in different documents. Thus, effective summarization methods that merge similar information across the documents are desirable. This paper introduces a clustered genetic semantic graph approach for multi-document abstractive summarization. The semantic graph from the document set is constructed in such a way that the graph vertices represent the predicate argument structures (PASs), extracted automatically by employing semantic role labeling (SRL); and the edges of graph correspond to semantic similarity weight determined from PAS-to-PAS semantic similarity, and PAS-to-document relationship. The PAS-to-document relationship is expressed by different features, weighted and optimized by genetic algorithm. The salient graph nodes (PASs) are ranked based on modified weighted graph based ranking algorithm. The clustering algorithm is performed to eliminate redundancy in such a way that representative PAS with the highest salience score from each cluster is chosen, and fed to language generation to generate summary sentences. Experiment of this study is performed using DUC-2002, a standard corpus for text summarization. Experimental results indicate that the proposed approach outperforms other summarization systems.