Computer Science
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This collection includes scholarly output from both faculty and students in Computer Science.
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Browsing Computer Science by Author "Al-Dhubhani, Raed Saeed"
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Item An adaptive geo-indistinguishability mechanism for continuous LBS queries(Springer US, 2018-11-01) Al-Dhubhani, Raed Saeed; Cazalas, JonathanThe 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 mechanisms 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. Geo-indistinguishability represents a formal notion of obfuscation-based location privacy which protects the user’s accurate location while allowing an adequate amount of information to be released to get the service with an accepted utility level. Despite its effectiveness and simplicity, geo-indistinguishability does not address the potential correlation of the subsequent locations reported within the continuous queries. In this paper, we investigate the effect of exploiting the correlation of the user’s obfuscated locations on the location privacy level. We propose an adaptive location preserving privacy mechanism that adjusts the amount of noise required to obfuscate the user’s location based on the correlation level with its previous obfuscated locations. The experiments show that adapting the noise based on the correlation level leads to a better performance by applying more noise to increase the location privacy level when required or by reducing the noise to improve the utility level.Item Correlation analysis for geo-indistinguishability based continuous LBS queries(IEEE, 2017) Al-Dhubhani, Raed Saeed; Cazalas, JonathanThe popularity of mobile devices with positioning capability and Internet accessibility in recent years has caused a revolution in the Location Based Services (LBS) market. Unfortunately, without preserving the user's location privacy, LBS providers can collect and log the accurate locations of the service users. Many mechanisms have been developed to preserve the LBS user's location privacy. While said mechanisms have had demonstrable effectiveness with snapshot queries, the shortcoming of supporting continuous queries is their main drawback. Geo-indistinguishability represents an obfuscation-based location privacy notion, which preserves the user's accurate location while allowing an adequate amount of information to be released. Despite its effectiveness and simplicity, geo-indistinguishability notion does not address the potential correlation of the subsequent locations reported within the continuous queries. In this paper, we report our progress on developing an adaptive geo-indistinguishability mechanism for continuous LBS queries. We show the effect of exploiting the correlation of the user's obfuscated locations on the location privacy level. The initial results show the need for an adaptive mechanism that adjusts the amount of noise required to obfuscate the user's location based on the correlation level with its previous obfuscated locations.Item A framework for preserving location privacy for continuous queries(Springer, 2019-09) Al-Dhubhani, Raed Saeed; Cazalas, Jonathan; Mehmood, Rashid; Katib, Iyad; Saeed, FaisalThe growth of the location-based services (LBSs) market in recent years was motivated by the widespread use of mobile devices equipped with positioning capability and Internet accessibility. To preserve the location privacy of LBS users, many mechanisms have been proposed to provide a partial disclosure by decreasing or blurring or the accuracy of the shared location. While these Location Privacy Preserving Mechanisms (LPPMs) have demonstrated effective performance with snapshot queries, this work shows that preserving location privacy for continuous queries should be addressed differently. In this paper, MOPROPLS framework is proposed with the aim to preserve location privacy in the specific case of continuous queries. As part of the proposed framework, a novel set of six requirements that any LPPM should meet in order to provide location privacy for continuous queries is proposed. In addition, a novel location privacy leakage metric and a novel two-phased probabilistic candidate selection algorithm are proposed. Comparing the performance of MOPROPLS framework with the geo-indistinguishability LPPM in terms of privacy (adversary estimation error) shows that the average of MOPROPLS framework improvement is 34%.