Database outsourcing is an emerging paradigm in cloud computing. The increase in spatial data has led organizations to upload their data onto third-party service providers. Outsourcing entails low cost, dynamic storage and high computational power, as it enables convenient, on-demand network access to a shared pool of computing resources (e.g., networks, servers, storage, applications). Giving control to untrusted third-party servers has security concerns such as confidentiality and privacy. Privacy concerns can be resolved by hiding database from server and attackers, hiding the distribution of spatial points in the space and protecting user query and its result. Therefore, we propose an approach based on cryptographic transformation to perform efficient and accurate query processing at the cloud server in order overcome the security concerns associated with cloud computing.
Nowadays, social network sites; such as Facebook and Twitter, have tremendous number of users in their repositories. Having this huge amount of data requires analyzing them to get statistics about the users and their interests. The information gained from mining social networks can be used in various applications such as recommendations, influence analysis and customer segmentation. The goal of the research group is to devise new techniques for extracting meaningful information inside today's social networks.
Sensor Networks (WSN) typically consists of a large number of small,
low-cost sensor nodes distributed over a large area with possibly one,
or more, powerful sink node gathering readings of sensor nodes. The
sensor nodes are tiny devices equipped with one or more sensors, some
processing circuits and a wireless transceiver. Each of these sensors
gathers its own observation of the environment which can be used with
other sensors' gathered data to provide a global view of the monitored
environment to a user. Many applications are developed using WSN in the
area of environmental and habitat monitoring, object and inventory
tracking, health and medical monitoring, battlefield observation and
industrial safety and control. In many of these applications, real-time
data mining of sensor data to promptly make intelligent decisions is
essential. Detecting phenomena in dynamic WSN environment where the
sensors are mobile and data clusters are dynamic is a
The popularity of social networks, such as Facebook, Twitter and Instagram, has dramatically increased during the last years, especially with the exponential growth in smartphones and mobile devices. Cyberbullying is an example of such threat impacting children, teenagers and young adults. It consists of posting aggressive and harassing messages, spreading rumors as well as sharing personal information, photographs and videos about the victims without their consent for the purpose of humiliating and making fun of them. Unfortunately, this threat is also becoming a significant issue in the UAE, especially with the wide adoption of Internet technologies and social media by UAE's population, mainly by the young generation. In this context, we aim in this research project at designing a framework for the detection and characterization of cyberbullying activities in online social networks. More precisely, we aim to identify relevant features and behaviors that characterize a cyberbullying threat and the involved users and design classifiers and algorithms to detect such threats.