Mobile CrowdSensing (MCS) is a new and attractive paradigm that offers an alternative solution to data collection. This paradigm has recently been used to aggregate data for Intelligent Transportation Systems (ITS) towards a smart city, as an alternative to traditional data collection associated with expensive sensors. However, as many users can collaborate on the sensing task, data reliability remains a major challenge for ITS, especially for visual content such as images. As a result, image forgery detection becomes an essential task in MCS applications, intending to guarantee image reliability. Its goal is to identify manipulated content which requires robust techniques. Many researchers addressed this using deep learning models and handcrafted features. With attention mechanisms’ success in the last few years, more researchers have adopted these techniques for accurate image forgery detection, motivated by the slogan "attention is all you need" [1]. However, as the proposed techniques rely usually on pixel-level supervision, and sometimes with the intervention of handcrafted features, it is therefore unclear whether attention mechanisms are able to focus and localize forged regions with no further guidance. This paper addresses this problem by studying the effectiveness of the attention mechanism in localizing forged regions. To this end, we designed a customized Convolutional Neural Network architecture, incorporating the proposed Dual Focus Attention Module. Next, the Grad-CAM technique [2] was employed to compare the ground truths of the forged images with the heat maps generated by Grad-CAM, to provide insights into the addressed problem.

Mobile Crowdsensing (MCS) is an effective sensing paradigm that promotes data acquisition and sharing among mobile devices by engaging a crowd of participants. However, MCS systems face two major challenges, dealing with unreliable participants, and preventing privacy leakage. This paper presents BlockCrowd, a privacy-preserving trust and reputation management system for MCS based on blockchain to address these challenges. First, smart contracts are used to enhance user engagement by creating a transparent and trustworthy system while preserving privacy. Second, a reputation management scheme is designed based on the quality of users' contributions to resist malicious activities. Finally, the task publishing and worker selection processes are based on user reputation metrics. BlockCrowd is implemented on the Ethereum blockchain using the solidity programming language. Security and performance evaluations revealed that BlockCrowd can effectively identify malicious participants while ensuring security and privacy.

With the advancement of technology, the collection of data used in Intelligent Transportation Systems has become increasingly easy, notably with the emergence of the Mobile CrowdSensing paradigm. This paradigm could provide insights into traffic situation, road condition, pedestrians' behaviours, public transportation situation, and so on. Through the use of the powerful sensors in the mobile, different types of data can be generated, such as Gyroscope data, light sensors data, Magnetometer data, GPS data, Accelerometer data, videos, and images. However, the use of MCS raises issues of data reliability, as it involves the participation of several mobile owners with different 'levels' of trustworthiness. An important related issue is image forgery, i.e. the manipulation of images in order to deliberately provide misleading information, which constitutes a threat to the decision-makers of the MSC-based applications in Intelligent Transportation Systems. In this paper, an overview on image forgery is presented. We examine the workflows, approaches and techniques employed for image forgery detection and localization. Then, we provide a brief review of some image splicing localization techniques. Finally, we provide a comparative analysis of handcrafted feature extraction-based techniques and deep learning-based techniques. The aim of this paper is to draw attention to the image forgery problem that could threaten any Mobile CrowdSensing application dedicated to image collection, and to provide an overview of different existing techniques that could be used to overcome this problem.

With the growing number of vehicles and increasing traffic complexity, the development of Intelligent Transportation Systems (ITS) has become essential in managing traffic flow, reducing congestion, and improving road safety. As a result, numerous research initiatives and projects have been launched in recent years in the Middle East and North African (MENA) countries. This paper presents a comprehensive review of the ITS projects funded in this region. We then evaluate these projects based on three criteria: security and data privacy, data analysis, and sensing techniques. The main objective is to identify common threads among these projects and explore potential opportunities for developing an open-source platform that can benefit the research community in the MENA region with similar research objectives.

The growth of Mobile Crowd Sensing has had an important impact on various industries, particularly Intelligent Transportation Systems, where it has been an efficient method for gathering data. Despite its advantages, there are significant privacy issues associated with MCS-based applications, which can result in the unauthorized exposure of users’ identities and locations. For this purpose, we explore the integration of MCS in the context of ITS, and its privacy concerns. This paper outlines the structure of MCS systems and provides an overview of their use in the context of ITS. It then discusses the privacy issues and measures related to MCS and conducts a literature review of privacy protection mechanisms using proposed privacy measures as evaluation criteria. Finally, the paper identifies open research challenges and future directions for the integration of privacy-preserving approaches in MCS applications within the ITS context, offering a comprehensive understanding and highlighting areas for further research.

Nearly most of the organizations store massive amounts of data in large databases for research, statistics, and mining purposes. In most cases, much of the accumulated data contain sensitive information belonging to individuals which may breach privacy. Hence, ensuring privacy in big data is considered a very important issue. The concept of privacy aims to protect sensitive information from various attacks that may violate the identity of individuals. Anonymization techniques are considered the best way to ensure privacy in big data. Various works have been already realized, taking into account horizontal clustering. The L-diversity technique is one of those techniques dealing with sensitive numerical and categorical attributes. However, the majority of anonymization techniques using L-diversity principle for hierarchical data cannot resist the similarity attack and therefore cannot ensure privacy carefully. In order to prevent the similarity attack while preserving data utility, a hybrid technique dealing with categorical attributes is proposed in this paper. Furthermore, we highlighted all the steps of our proposed algorithm with detailed comments. Moreover, the algorithm is implemented and evaluated according to a well-known information loss-based criterion which is Normalized Certainty Penalty (NCP). The obtained results show a good balance between privacy and data utility.

IT professionals are overwhelmed by rapidly-changing technology and growing complexity. Additional challenges are introduced by cyber-security. Self-protecting software tries to alleviate this situation by combining principles and techniques from both autonomic computing and software security. However, this combination creates scalability issues, as well as cross-cutting concerns. In this work, we present Immunizer: A Scalable Loosely-Coupled Self-Protecting Software Framework. Immunizer extends our Application-level Unsupervised Outlier-based Intrusion Detection and Prevention Framework by leveraging the architectural building blocks of autonomic computing, and adopting a microagent/microservice architectural model, augmented with distributed cluster computing, for maximum scalability and separation of concerns. More specifically, we design each of the Monitor, Analyze, Plan and Execute functions of the autonomic MAPE-K control loop as a parallelized microservice, while we model its Knowledge function as a data streaming, caching and storage infrastructure. Moreover, we design the Sensor and Effector touchpoint modules as adaptive lightweight runtime application instrumentation microagents.

Patients' privacy preferences have a direct impact on their health-related behavior. The fear of violating their privacy while using information and communication technologies to manage their health records may lead to some negative comportment. Motivated by this and based on the theory of planned behavior, we present in this paper an approach aiming to predict patient's future behavior based on both his expressed privacy preferences and how much the patient trusts the entities involved in his care. By predicting his future behavior, we can offer a personalized recommendation service to help the patient having a much positive health behavior. To conclude the paper, a case study is presented showing how our approach can be applied.

As cyber threats are permanently jeopardizing individuals privacy and organizations’ security, there have been several efforts to empower software applications with built-in immunity. In this paper, we present our approach to immune applications through application-level, unsupervised, outlier-based intrusion detection and prevention. Our framework allows tracking application domain objects all along the processing lifecycle. It also leverages the application business context and learns from production data, without creating any training burden on the application owner. Moreover, as our framework uses runtime application instrumentation, it incurs no additional cost on the application provider. We build a fine-grained and rich-feature application behavioral model that gets down to the method level and its invocation context. We define features to be independent from the variable structure of method invocation parameters and returned values, while preserving security-relevant information. We implemented our framework in a Java environment and evaluated it on a widely-used, enterprise-grade, and open-source ERP. We tested several unsupervised outlier detection algorithms and distance functions. Our framework achieved the best results in terms of effectiveness using the Local Outlier Factor algorithm and the Clark distance, while the average instrumentation overhead per intercepted call remains acceptable.

The rapid growth of Internet and business globalization has lead organizations to collaborate in order to reach common goals through creating inter-organizational workflows. This collaboration poses new security challenges, particularly the cohabitation of different security policies of participating organizations in the workflow. In fact, organizations could have different or even conflicting policies. How to conciliate different local policies and create new coherent global policy free of conflict? How to resolve detected policy conflict? In this paper, we propose a new approach in order to respond to these two issues. This approach is based on the organization weight to resolve detected policy conflict in inter-organizational workflows.

Today, workflow systems which aim the automation of a business process involving the coordinated execution of multiple tasks performed by different entities have experienced an increase use. Unfortunately, little workflow management systems (WFMS) take into account access control constraints such as separation of duties (SoD). In this paper, we present a new approach, RB-WAC (role-based workflow access control), to participate in amending the above shortcomings. This approach which is based on the standard of access control RBAC, introduces new rules to detect potential conflicts related to a workflow instance and also suggests the use of the concept of priority in order to resolve these conflicts.

Applications such those of electronic payment require the participants authentication and a privacy of crucial information. Public-key infrastructures (PKIs) are essential for providing them these security services in open networks like the Internet. We propose logic for reasoning about PKI trust models. Our formalism enables us to describe a trust model of a PKI with greater precision than the widely used graph. It also allows us to verify whether a model respond to the PKI objectives and under which conditions. These objectives generally concern statements about entitles beliefs with regard to public key authenticity and certification authorities (CAs) trustworthiness. The proposed formalism takes into account the number of intermediates that have participated in an entity belief and the constraints that can be the concern of certification practices or certificate policies of PKI's CAs. These constraints may influence the trust model suitability to applications with specific requirements.

We propose a logic-based approach for reasoning about (public-key infrastructure) PKI trust models. Our formalism uses the predicate calculus language to describe a PKI trust model with greater precision than the widely used graph. It allows us to formalize the certificates and the statements about entities beliefs with regard to public key authenticity and certification authority's (CA's) trustworthiness. In this formalism, we take into account the number of CAs that have participated in an entity belief, the trust level in a statement and the policies constraints. By using this approach, we can verify the suitability of a model to applications with particular requirements.