Providing Location Privacy for the Users of Location-based Services

Location-based services (LBS) are becoming more popular due to the growing usage of smartphones. They serve their users with various location-based services by having access to their location information. Privacy of users is in danger if their exact location data is exposed over time since their mobility patterns and their usual visiting spots such as their homes or work places can be revealed in the long term. Some mechanisms are proposed to protect  user's location privacy and one of the most common ones is K-anonymity. In this mechanism, K different user's location information who are around the same area are changed to a common value which make them indistinguishable from each other's. The privacy level that is provided by this mechanism is usually measured by its metric named K-anonymity metric. Reza Shokri et al.[36] questions the effectiveness of K-anonymity metric in reflecting the real location privacy provided by K-anonymity mechanism in presence of different adversaries. In this thesis, we have studied different mechanisms which provide location privacy, implemented a new version of K-anonymity which emphasizes on more privacy and measured the effect of number of requests and number of users on the location privacy by using Distortion-based metric, which is a novel metric proposed by Reza Shokri et al.[35] and they believe it covers all the shortcomings of the previously proposed metrics. We have, as well, analysed and studied the application of this novel metric in some real-world scenarios

Providing Location Privacy for the Users of Location-based Services

Location-based services (LBS) are becoming more popular due to the growing usage of smartphones. They serve their users with various location-based services by having access to their location information. Privacy of users is in danger if their exact location data is exposed over time since their mobility patterns and their usual visiting spots such as their homes or work places can be revealed in the long term. Some mechanisms are proposed to protect  user's location privacy and one of the most common ones is K-anonymity. In this mechanism, K different user's location information who are around the same area are changed to a common value which make them indistinguishable from each other's. The privacy level that is provided by this mechanism is usually measured by its metric named K-anonymity metric. Reza Shokri et al.[36] questions the effectiveness of K-anonymity metric in reflecting the real location privacy provided by K-anonymity mechanism in presence of different adversaries. In this thesis, we have studied different mechanisms which provide location privacy, implemented a new version of K-anonymity which emphasizes on more privacy and measured the effect of number of requests and number of users on the location privacy by using Distortion-based metric, which is a novel metric proposed by Reza Shokri et al.[35] and they believe it covers all the shortcomings of the previously proposed metrics. We have, as well, analysed and studied the application of this novel metric in some real-world scenarios