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Document Type |
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Thesis |
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Document Title |
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SHARING-BASED LOCATION PRIVACY PRESERVING FRAMEWORK FOR CONTINUOUS LOCATION BASED SERVICES إطار عمل مبني على المشاركة للحفاظ على خصوصية المكان للاستعلامات المستمرة للخدمات المعتمدة على المكان |
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Subject |
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Faculty of Computing and Information Technology |
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Document Language |
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Arabic |
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Abstract |
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The popularity of mobile devices with positioning capability and Internet accessibility in recent years has led to a revolution in the Location-based services (LBSs) market. Unfortunately, without preserving the user's location privacy, LBS providers can collect and log the accurate location data of the service users and provide them to third parties. Many location privacy preserving mechanisms (LPPMs) have been proposed to preserve the LBS user’s location privacy. These mechanisms provide a partial disclosure of the user’s location. While said mechanisms have had demonstrable effectiveness with snapshot queries, the shortcoming of supporting continuous queries is their main drawback. This shortcoming is a result of the lack of ability to effectively measure and react to the privacy leakage produced by continuous queries. Continuous queries make location privacy preservation difficult due to the privacy leakage produced by correlating the user’s reported locations. In this work, we aim to preserve user location privacy in the case of continuous LBS queries. To achieve that, we propose a framework, namely MOdeling and REacting to Privacy Leakage Sources (MOREPLS). As part of the framework, firstly, we propose a novel set of six requirements that any LPPM should meet in order to provide location privacy for continuous LBS queries. Secondly, we propose a novel location privacy metric that is capable of measuring location privacy leakage of continuous LBS queries. Thirdly, the framework includes a novel two-phased probabilistic candidate selection algorithm that takes into consideration the correlation between the obfuscated locations in order to preserve privacy for continuous queries. We implement our framework as a desktop application and as an Android App, and evaluate it using a real world dataset (Epfl/mobility). The performance for the framework is compared with the geo-indistinguishability LPPM in terms of privacy (adversary estimation error) and the reported improvements average is 34%. Finally, to give an outlook, we leverage on the MOREPLS framework to propose a location privacy preservation system for smart cities. |
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Supervisor |
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Dr. Iyad Katib |
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Thesis Type |
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Doctorate Thesis |
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Publishing Year |
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1439 AH
2017 AD |
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Co-Supervisor |
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Prof. Rashid Mehmood |
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Added Date |
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Wednesday, December 13, 2017 |
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Researchers
| رائد سعيد الذبحاني | Al-Dhubhani, Raed Saeed | Researcher | Doctorate | |
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