A Framework for Preserving Location Privacy for Continuous Queries

Al-Dhubhani, Raed; Cazalas, Jonathan; Mehmood, Rashid; Katib, Iyad; Saeed, Faisal

doi:10.1007/978-3-030-33582-3_77

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…However, generally speaking, it is possible to detect information from Twitter data that affects individuals’ privacy. Our earlier works [ 57 , 88 , 89 ] have looked into privacy and we plan to extend this to investigate Twitter data privacy in the future.…”

Section: Discussionmentioning

confidence: 99%

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

Alomari

Katib

Albeshri

et al. 2021

Sensors

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Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The proposed method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. We also use in this work a location extractor developed by us that allows us to extract and visualize spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using support vector machines, naïve Bayes, and logistic regression-based classifiers, we are able to detect and validate several real events in Saudi Arabia without prior knowledge, including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings show the effectiveness of Twitter media in detecting important events with no prior knowledge about them.

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Section: Discussionmentioning

confidence: 99%

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

Alomari

Katib

Albeshri

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…[82,83,[208][209][210][211][212]. Monitoring and tracing people and their activities are important; however, related privacy and security concerns must also be addressed [196,197]. All of these areas need further investigation.…”

Section: Discussionmentioning

confidence: 99%

“…Governments around the world and the international community must work together to develop policies and instruments to protect individuals' privacy while managing pandemics. Some of our earlier works [196][197][198] have investigated this topic and we plan to address these issues in the future.…”

Section: Digitally Born Data and Interdisciplinary Research: Dilemmas Limitations And Solutionsmentioning

confidence: 99%

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

et al. 2021

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SARS-CoV-2, a tiny virus, is severely affecting the social, economic, and environmental sustainability of our planet, causing infections and deaths (2,674,151 deaths, as of 17 March 2021), relationship breakdowns, depression, economic downturn, riots, and much more. The lessons that have been learned from good practices by various countries include containing the virus rapidly; enforcing containment measures; growing COVID-19 testing capability; discovering cures; providing stimulus packages to the affected; easing monetary policies; developing new pandemic-related industries; support plans for controlling unemployment; and overcoming inequalities. Coordination and multi-term planning have been found to be the key among the successful national and global endeavors to fight the pandemic. The current research and practice have mainly focused on specific aspects of COVID-19 response. There is a need to automate the learning process such that we can learn from good and bad practices during pandemics and normal times. To this end, this paper proposes a technology-driven framework, iResponse, for coordinated and autonomous pandemic management, allowing pandemic-related monitoring and policy enforcement, resource planning and provisioning, and data-driven planning and decision-making. The framework consists of five modules: Monitoring and Break-the-Chain, Cure Development and Treatment, Resource Planner, Data Analytics and Decision Making, and Data Storage and Management. All modules collaborate dynamically to make coordinated and informed decisions. We provide the technical system architecture of a system based on the proposed iResponse framework along with the design details of each of its five components. The challenges related to the design of the individual modules and the whole system are discussed. We provide six case studies in the paper to elaborate on the different functionalities of the iResponse framework and how the framework can be implemented. These include a sentiment analysis case study, a case study on the recognition of human activities, and four case studies using deep learning and other data-driven methods to show how to develop sustainability-related optimal strategies for pandemic management using seven real-world datasets. A number of important findings are extracted from these case studies.

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PISA: A proximity-based social networking (PBSN) protection model

Ramtohul

Khedo

2022

Secur J

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A Framework for Preserving Location Privacy for Continuous Queries

Cited by 4 publications

References 41 publications

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

PISA: A proximity-based social networking (PBSN) protection model

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