volume-15-Issue 2 (2021)
An Effective and Efficient Framework for Fast Privacy-Preserving Keyword Search on Encrypted Outsourced Cloud Data
JUSPN, volume-15, Issue 2 (2021) , PP 43 - 53
Published: 24 Dec 2021
by Alfredo Cuzzocrea, Carson Leung, S. Sourav, Bryan H. Wodi from iDEA Lab, University of Calabria, Rende, Italy & LORIA, Nancy, France - Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada
Abstract: Cloud providers offer storage as a service to the data owners to store emails and files on the cloud server. However, sensitive data should be encrypted before storing on the cloud server to avoid privacy concerns. With the encryption of documents, it is not feasible for data owners to retrieve documents based on keyword search as they can do with plain text documents. Hence, it is desirable to perform a multi-keyword search on encrypted data. To achieve this goal, we present a fast privacy-preserving model for keyword search on encrypted outsourced data in this paper. Specifically, the model first performs a keyword search on encrypted data and checks its support for dynamic operations. Based on keyword search results, it then sorts all the relevant data documents using the number of keywords matched for a given query. To evaluate its performance of our model, we applied the standard metrics like precision and recall. The results show the effectiveness of our privacy-preserving keyword search on encrypted outsourced data. read more... read less...
Keywords: Big Data, Big Data Privacy, Big Data Security, Encryption, Privacy-Preserving Keyword Search, PrivacyPreserving Information Retrieval
JUSPN, volume-15, Issue 2 (2021) , PP 33 - 41
Published: 24 Dec 2021
by Wendy Osborn from Department of Mathematics and Computer Science, University of Lethbridge, Lethbridge, Alberta, Canada, T1K 3M4
Abstract: In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified. read more... read less...
Keywords: spatial data streams, stream query processing, spatial join, performance
JUSPN, volume-15, Issue 2 (2021) , PP 25 - 31
Published: 21 Dec 2021
by Karim Haricha, Azeddine Khiat, Yassine Issaoui, Ayoub Bahnasse, Hassan Ouajji from Lab SSDIA, University Hassan II of Casablanca, Morocco, ENSAM Casablanca, University Hassan II of Casablanca, Morocco
Abstract: Production activities is generating a large amount of data in different types (i.e., text, images), that is not well exploited. This data can be translated easily to knowledge that can help to predict all the risks that can impact the business, solve problems, promote efficiency of the manufacture to the maximum, make the production more flexible and improving the quality of making smart decisions, however, implementing the Smart Manufacturing(SM) concept provides this opportunity supported by the new generation of the technologies. Internet Of Things (IoT) for more connectivity and getting data in real time, Big Data to store the huge volume of data and Deep Learning algorithms(DL) to learn from the historical and real time data to generate knowledge, that can be used, predict all the risks, problem solving, and better decision-making. In this paper, we will introduce SM and the main technologies to success the implementation, the benefits, and the challenges. read more... read less...
Keywords: Smart Manufacturing (SM), Industry 4.0, Internet Of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL).
JUSPN, volume-15, Issue 2 (2021) , PP 19 - 24
Published: 19 Dec 2021
by Vishv Patel, Devansh Shah, Nishant Doshi from Pandit Deendayal Energy University, Gandhinagar, India, Gujarat-382426
Abstract: The large deployment of the Internet of Things (IoT) is empowering Smart City tasks and activities everywhere throughout the world. Items utilized in day-by-day life are outfitted with IoT devices and sensors to make them interconnected and connected with the internet. Internet of Things (IoT) is a vital piece of a smart city that tremendously impact on all the city sectors, for example, governance, healthcare, mobility, pollution, and transportation. This all connected IoT devices will make the cities smart. As different smart city activities and undertakings have been propelled in recent times, we have seen the benefits as well as the risks. This paper depicts the primary challenges and weaknesses of applying IoT innovations dependent on smart city standards. Moreover, this paper points the outline of the technologies and applications of the smart cities. read more... read less...
Keywords: Internet of Things (IoT), Smart Cities, IoT Devices and Sensors, Technologies of Smart Cities, Applications of Smart Cities
JUSPN, volume-15, Issue 2 (2021) , PP 11 - 17
Published: 15 Dec 2021
by Meryem Elmoulat, Olivier Debauche, Saïd Mahmoudi, Sidi Ahmed Mahmoudi, Adriano Guttadauria, Pierre Manneback, Frédéric Lebeau from University Mohammed V, Faculty of Sciences, Research Unit GeoRisk: Geological Risks, Battouta Avenue, Rabat, Morocco, 10140, University of Mons, Faculty of Engineering - ILIA/Infortech, Place du Parc 20, Mons, Belgium, 7000, University of Liège - GxABT, TERRA, Passage des déportés 2, Gembloux, Belgium, 5030, University of Liège - GxABT, BioDynE, Gembloux, Belgium, 5030
Abstract: Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models. read more... read less...
Keywords: Landslides Susceptibility, IoT, Artificial Intelligence, Early Warning System, Landslides Monitoring, Edge AI, Edge IoT
AI’S Contribution to Ubiquitous Systems and Pervasive Networks Security – Reinforcement Learning vs Recurrent Networks
JUSPN, volume-15, Issue 2 (2021) , PP 01 - 09
Published: 12 Dec 2021
by Christophe Feltus from Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg, L-4362
Abstract: Reinforcement learning and recurrent networks are two emerging machine-learning paradigms. The first learns the best actions an agent needs to perform to maximize its rewards in a particular environment and the second has the specificity to use an internal state to remember previous analysis results and consider them for the current one. Research into RL and recurrent network has been proven to have made a real contribution to the protection of ubiquitous systems and pervasive networks against intrusions and malwares. In this paper, a systematic review of this research was performed in regard to various attacks and an analysis of the trends and future fields of interest for the RL and recurrent network-based research in network security was complete. read more... read less...
Keywords: Artificial Intelligence, Reinforcement Learning, Recurrent Networks, RNN, GRU, LSTM, RL, Security, Network Security, Malware Detection, Literature Review, Machine learning, Intrusion Detection, State of the Art.