Location-based services (LBSs) are currently some of the fastest growing information technology industries. User privacy in LBSs has attracted great interest in the research community. However, the proliferation of position identifying devices has become an increasing privacy threat for users in LBSs. It is very difficult to avoid the privacy threat of a user in processing his/her request because the user has to submit his/her exact location with a query to the LBS. To protect privacy in road networks, the existing method employs an X-Star framework to hide the query issuer and provide protection from attack resilience. However, it incurs low anonymization success rate and high computation cost. To solve the problems, we propose a Hilbertorder-based star network expansion cloaking algorithm (H-Star). Our H-Star guarantees k-anonymity under the strict reciprocity condition and increases anonymization success rate by reducing computation overhead. We also propose k-nearest neighbor and range query processing algorithms based on the anonymized region. Through comprehensive experimental analysis, we show the effectiveness of our algorithms in the field of spatial cloaking. different road segments. The cloaker sends cloaked region to the LBS. The LBS retrieves a result set that is guaranteed to contain the query results for any possible user position inside the blur area. When the anonymizer receives a result set, it filters and sends the exact answer to the user. The second group includes techniques without a trusted third party [15-18] called distributed system, where users make a group and select a group head (GH) among them and GH is responsible for handling communication between the users and LBS.However, most existing privacy approaches cannot satisfy users' privacy when they travel in a road network. This is because they consider Euclidean space rather than the road network. Recently,[18][19][20][21] has been proposed to point out this type of problem wherein a cloaking area is created based on road segments rather than a squareshaped cloaking region. Among them, X-Star [14] provides a star-graph-based cloaking model that can protect user's privacy from attack resilience and optimize query processing cost. However, in X-Star, anonymization success rate is very low and computation overhead is quite high. Therefore, in this paper, we propose a Hilbertorderbased star network expansion cloaking algorithm (H-Star), which can protect user's privacy even though adversaries are physically present in a network. The proposed H-Star fulfills reciprocity property, that is, each cloaking region must be shared by at least k users, to preserve user's privacy. It can increase anonymization success rate and decrease computation overhead. To the best of our knowledge, this is the first work that can ensure the above requirements at the same time. We also propose k-nearest neighbor and range query processing algorithms based on the anonymized region. Our contributions can be summarized as follows:We propose a k-anonymitybased clo...
The proliferations of position identifying devices become increasing privacy threat in location-based services (LBSs). It is very difficult to avoid the privacy threat of a user in processing his/her request because the user has to submit his/her exact location with a query to the LBS. To protect privacy in road networks, the existing method employs a XStar framework to hide the query issuer and provide protection from attack resilience. However, it incurs low anonymization success rate and computation cost is quite high. To address these issues, we propose Hilbert-order based star network expansion cloaking algorithm (H-Star). Our H-Star guarantees K-anonymity under the strict reciprocity condition and increases anonymization success rate by reducing computation overhead. Through comprehensive experimental evaluations, we show the effectiveness of our algorithm in the field of spatial cloaking.
The ubiquity of GPS enabled smartphones with Internet connectivity has resulted in the widespread development of location-based services (LBSs). People use these services to obtain useful advises for their daily activities. For example, a user can open a navigation app to find a route that results in the shortest driving time from the current location to a destination. Nevertheless, people have to reveal location information to the LBS providers to leverage such services. Location information is sensitive since it can reveal habits about an individual. LBS providers are aware of this and take measures to protect user privacy. One well established and simple approach is to remove GPS data from user data working with the assumption that it will lead to a high degree of privacy. In this thesis, we challenge this notion of removing location information while retaining other features would lead to a high degree of location privacy. We find that it is possible to reconstruct the original routes by analyzing just the turn instructions provided to a user by a navigation service. We evaluated our approach using both synthetic and real road network data and demonstrate the effectiveness of this new attack in a range of realistic scenarios. i I take this opportunity to thank the University of Melbourne for granting me an IPRS and APA (Int) scholarships, which helped me to attend one of the most sought after Universities in the world and allowing me to stay focused on this research without facing financial hardships. My sincere gratitude goes to the staff of the Department of Computing and Information Systems for providing a rich and friendly academic environment to conduct this research work successfully. Finally, I would like to express my gratitude to Almighty for His mercy that allows me to successfully accomplish the Mphil.
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