Location Privacy in Pervasive Computing
IEEE Pervasive Computing
Protecting Location Privacy Through Path Confusion
SECURECOMM '05 Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks
Enhancing Security and Privacy in Traffic-Monitoring Systems
IEEE Pervasive Computing
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Preserving privacy in gps traces via uncertainty-aware path cloaking
Proceedings of the 14th ACM conference on Computer and communications security
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
Perfect Matching Disclosure Attacks
PETS '08 Proceedings of the 8th international symposium on Privacy Enhancing Technologies
Identification via location-profiling in GSM networks
Proceedings of the 7th ACM workshop on Privacy in the electronic society
On the Anonymity of Home/Work Location Pairs
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
A survey of computational location privacy
Personal and Ubiquitous Computing
A distortion-based metric for location privacy
Proceedings of the 8th ACM workshop on Privacy in the electronic society
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Privacy vulnerability of published anonymous mobility traces
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Unraveling an old cloak: k-anonymity for location privacy
Proceedings of the 9th annual ACM workshop on Privacy in the electronic society
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
A formal model of obfuscation and negotiation for location privacy
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Evaluating the privacy risk of location-based services
FC'11 Proceedings of the 15th international conference on Financial Cryptography and Data Security
Measuring query privacy in location-based services
Proceedings of the second ACM conference on Data and Application Security and Privacy
The impact of trace and adversary models on location privacy provided by K-anonymity
Proceedings of the First Workshop on Measurement, Privacy, and Mobility
Protecting location privacy: optimal strategy against localization attacks
Proceedings of the 2012 ACM conference on Computer and communications security
Combining social authentication and untrusted clouds for private location sharing
Proceedings of the 18th ACM symposium on Access control models and technologies
Optimal sporadic location privacy preserving systems in presence of bandwidth constraints
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
Privacy vulnerability of published anonymous mobility traces
IEEE/ACM Transactions on Networking (TON)
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Mobile users expose their location to potentially untrusted entities by using location-based services. Based on the frequency of location exposure in these applications, we divide them into two main types: Continuous and Sporadic. These two location exposure types lead to different threats. For example, in the continuous case, the adversary can track users over time and space, whereas in the sporadic case, his focus is more on localizing users at certain points in time. We propose a systematic way to quantify users' location privacy by modeling both the location-based applications and the location-privacy preserving mechanisms (LPPMs), and by considering a well-defined adversary model. This framework enables us to customize the LPPMs to the employed location-based application, in order to provide higher location privacy for the users. In this paper, we formalize localization attacks for the case of sporadic location exposure, using Bayesian inference for Hidden Markov Processes. We also quantify user location privacy with respect to the adversaries with two different forms of background knowledge: Those who only know the geographical distribution of users over the considered regions, and those who also know how users move between the regions (i.e., their mobility pattern). Using the Location-Privacy Meter tool, we examine the effectiveness of the following techniques in increasing the expected error of the adversary in the localization attack: Location obfuscation and fake location injection mechanisms for anonymous traces.