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Recently, the research of Internet of Things (IoT) and Multimedia Big Data (MBD) has been growing tremendously. Both IoT and MBD have a lot of multimedia data, which can be tampered easily. Therefore, the research of multimedia forensics is necessary. Copy-move is an important branch of multimedia forensics. In this paper, a novel copy-move forgery detection scheme using combined features and transitive matching is proposed. First, SIFT and LIOP are extracted as combined features from the input image. Second, transitive matching is used to improve the matching relationship. Third, a filtering approach using image segmentation is proposed to filter out false matches. Fourth, affine transformations are estimated between these image patches. Finally, duplicated regions are located based on those affine transformations. The experimental results demonstrate that the proposed scheme can achieve much better detection results on the public database under various attacks.

The paper introduces an approach to privacy enhancing technologies that sees privacy not merely as an individual right, but as a public good. This understanding of privacy has recently gained ground in the debate on appropriate legal protection for privacy in an online environment. The jurisprudential idea that privacy is a public good and prerequisite for a functioning democracy also entails that its protection should not be left exclusively to the individual whose privacy is infringed. This idea finds its correspondence in our approach to privacy protection through obfuscation, where everybody in a group takes a small privacy risk to protect the anonymity of fellow group members. We show how these ideas can be computationally realised in an Investigative Data Acquisition Platform (IDAP). IDAP is an efficient symmetric Private Information Retrieval (PIR) protocol optimised for the specific purpose of facilitating public authorities’ enquiries for evidence.

Broadcast is a common operation used in Mobile Ad hoc Networks (MANETs) for many services, such as, routdiscovery and sending an information messages. The direct method to perform broadcast is simple flooding, which itcan dramatically affect the performance of MANET. Recently, a probabilistic approach to flooding has beenproposed as one of most important suggested solutions to solve the broadcast storm problem, which leads to thecollision, contention and duplicated messages. This paper proposed new probabilistic method to improve theperformance of existing on-demand routing protocol by reduced the RREQ overhead during rout discoveryoperation. The simulation results show that the combination of AODV and a suitable probabilistic rout discoverycan reduce the average end- to- end delay as well as overhead and still achieving low normalized routing load,comparing with AODV which used fixed probability and blind flooding

Machine learning (ML) driven solutions have been widely used to secure wireless communications Vehicular ad hoc networks (VANETs) in recent studies. Unlike existing works, this paper applies support vector machine (SVM) for intrusion detection in VANET. The structure of SVM has many computation advantages, such as special direction at a finite sample and irrelevance between the complexity of algorithm and the sample dimension. Intrusion detection in VANET is nonconvex and combinatorial problem. Thus, three intelligence optimization algorithms are used for optimizing the accuracy value of SVM classifier. These optimization algorithms include Genetic algorithm (GA), Particle swarm optimization (PSO), and ant colony optimization (ACO). Our results demonstrate that GA outperformed other optimization algorithms.

This paper discusses privacy enhancing technologies in the field of inter-agency data sharing, a key government objective for efficient public service delivery. We analyse the legal and conceptual framework that governs multi-agency cooperation in particular in the field of child protection work, and develop two approaches to represent relevant data protection ideas computationally in the infrastructure that agencies use to exchange sensitive personal data.