Protecting personal data has become a major issue of concern in the whole world. As per the General Data Protection Regulation (GDPR), Data Owners (DO) are given back control over their data, which is maintained by the Service Providers (SP). However, SP needs to be fully GDPR compliant. In the existing GDPR enforcement technologies, DO is severely facing the scalability issue considering it has to manage separate keys for different data. Clearly, it would be highly impractical to manage such a huge and proportionally increased amount of keys for resource constrained devices of DO. Furthermore, the integrity of the data received by the Data User (DU) is not addressed in the existing works. We propose a proxy re-encryption based secure protocol where DO has to manage only one key. We propose a threat model to analyze our proposed protocol. In this analysis, we use a random oracle model to assess security and privacy. We use Burrows-Abadi-Needham (BAN) logic to verify the correctness. Simulation through AVISPA and ProVerif, formal verification tools, also confirms the security and privacy of the proposed protocol. A testbed simulation is conducted to estimate the computational, communication, and storage requirements, ensuring the practicality of the proposed protocol.

An autonomous Vehicle to Vehicle (V2V) communication mode, which allows V2V communication in out of eNB (Evolved Node B) coverage areas, has been recently introduced into the Long Term Evolution (LTE) standard. Recent research has studied the performance of this LTE V2V autonomous mode for an urban intersection use case, where non-line-of-sight (NLOS) communication links are present and it is observed that the overall message transmission performance in LTE V2V is found to degrade drastically in NLOS link conditions. To improve the performance, a vehicle-assisted relaying strategy was proposed. In this scheme, at each time instance, distance of each vehicle from intersection point is computed. Then the vehicle close to the intersection point is nominated as relaying vehicle and will rebroadcast the BSMs (Basic Safety Message) in a selective manner. However, security aspects while relaying the BSM is not being addressed in the suggested model. We cannot reject the possibility that a vehicle can be malicious. A malicious vehicle can manipulate its distance from the intersection point, thereby nominating itself as the relaying vehicle with malicious motives such as to use extra infrastructural faciltiy, curiosity in the messages sent by other vehicles, etc. This work proposes a secure algorithm for finding the most appropriate relaying vehicle to address this security vulnerability so that a vehicle can no longer falsify its location information.

In n-party Computation, data from different sources are integrated to achieve a common goal, which can be beneficial for different applications. However, this goal may be at risk due to the fact that this corresponding data can be the target of attacks from outside attackers as well as inside ones. On the other hand, in Secure n-party Computation, the data can be processed by preserving security and privacy in such a way that no party can know the data of the others. Several schemes have been proposed to address the issues regarding n-party computation. However, some of them are not efficient in terms of computation and communication, or some of them have used a Trusted Initializer (TI), a third party, which is considered as trusted, for communicating between different parties. Therefore, to address these issues, we propose an n-party secure computation protocol without using any Trusted Initializer (TI). We formally simulate our protocol using Automated Verification of Internet Security Protocols and Applications (AVISPA) as well as we elaborate on the formal and informal security analysis of our proposed protocol. Performance analysis of our proposed protocol is also carried out and it is observed our proposed protocol is secure and protects the privacy of the system.

With emergence of smart city concept, intelligent transportation becomes one of its key enablers, with navigation as an indispensable component. Vehicular ad hoc network (VANET) based systems emerge to provide navigation services to vehicle users, which is characterized by employing Roadside Units (RSUs) to collect road condition information from nearby vehicles. The querying vehicle exploits the information retrieved through the RSUs to determine an optimal route. We focus on addressing two key issues in such system. First, while querying, the destination of the vehicle user should not be leaked to the queried RSU and trusted authority (TA), which is likely to be misused. Second, the navigation process should be independent of participation of the centralized TA, which can be a single point of failure. To address these issues, this article proposes a security and privacy preserved intelligent vehicle navigation system, adopting a distributed approach, to retrieve navigation without revealing the destination. The security of the system has been thoroughly evaluated using an adversary model, BAN Logic and AVISPA tool. In addition, the performance of the proposed system has been evaluated, and compared with the existing systems. The comparison illustrates that the proposed system is computationally efficient than the existing systems.

Malaria, a life-threatening disease, develops due to the bite of female Anopheles mosquito. It spreads the plasmodium parasites in human blood, killing hundreds of millions of people every year. Modern scientific advancements play a pivotal role to combat the disease, along with biomedical research by the medical experts to possibly eradicate this disease from all parts of the world. With the significant development in deep learning research, faultless identification of medical imaging has become an important factor in medical diagnosis and decision-making. To this end, we present a deep learning based approach using a convolutional neural network for detecting malaria from microscopic cell images using image classification. The proposed CNN model implemented using 5-fold cross validation approach outperforms all the existing methods in terms of accuracy and other evaluation metrics, thus achieving the best results till date in malaria detection using deep learning.

A smell in software refers to a symptom introduced in software artifacts such as architecture, design or code. A code smell can potentially cause deeper and serious problems, while dealing with mainly non-functional requirements such as testability, maintainability, extensibility and scalability. The detection of code smell is an essential step in the refactoring process, which facilitates non functional requirements in a software. The existing approaches for detecting code smells use detection rules or standards using a combination of different object-oriented metrics. Although a variety of code smell detection tools have been developed, they still have limitations and constraints in their capabilities. The most well-known object-oriented metrics are considered to identify the presence of smells in software. This paper proposes a deep learning based approach to detect two code smells (Brain Class and Brain Method). The proposed system uses thirty open source Java projects, which are shared by many users in GitHub repositories. The dataset of these Java projects is partitioned into mutually exclusive training and test sets. Our experiments have demonstrated high accuracy results for both the code smells.