Location Privacy in Pervasive Computing
IEEE Pervasive Computing
k-anonymity: a model for protecting privacy
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IEEE Security and Privacy
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ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
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SIGSPATIAL Special
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Personal and Ubiquitous Computing
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PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
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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
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Mining and modeling large scale cell phone data: invited talk
FOMC '11 Proceedings of the 7th ACM ACM SIGACT/SIGMOBILE International Workshop on Foundations of Mobile Computing
Anonymizing geo-social network datasets
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
The loss of location privacy in the cellular age
Communications of the ACM
Human mobility modeling at metropolitan scales
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Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Middleware for location privacy: an overview
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Inferring human mobility patterns from anonymized mobile communication usage
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
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CHI '13 Extended Abstracts on Human Factors in Computing Systems
Exploiting innocuous activity for correlating users across sites
Proceedings of the 22nd international conference on World Wide Web
Active tracking in mobile networks: An in-depth view
Computer Networks: The International Journal of Computer and Telecommunications Networking
ACM SIGMOBILE Mobile Computing and Communications Review
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We examine a very large-scale data set of more than 30 billion call records made by 25 million cell phone users across all 50 states of the US and attempt to determine to what extent anonymized location data can reveal private user information. Our approach is to infer, from the call records, the "top N" locations for each user and correlate this information with publicly-available side information such as census data. For example, the measured "top 2" locations likely correspond to home and work locations, the "top 3" to home, work, and shopping/school/commute path locations. We consider the cases where those "top N" locations are measured with different levels of granularity, ranging from a cell sector to whole cell, zip code, city, county and state. We then compute the anonymity set, namely the number of users uniquely identified by a given set of "top N" locations at different granularity levels. We find that the "top 1" location does not typically yield small anonymity sets. However, the top 2 and top 3 locations do, certainly at the sector or cell-level granularity. We consider a variety of different factors that might impact the size of the anonymity set, for example the distance between the "top N" locations or the geographic environment (rural vs urban). We also examine to what extent specific side information, in particular the size of the user's social network, decrease the anonymity set and therefore increase risks to privacy. Our study shows that sharing anonymized location data will likely lead to privacy risks and that, at a minimum, the data needs to be coarse in either the time domain (meaning the data is collected over short periods of time, in which case inferring the top N locations reliably is difficult) or the space domain (meaning the data granularity is strictly higher than the cell level). In both cases, the utility of the anonymized location data will be decreased, potentially by a significant amount.