Location Privacy in Pervasive Computing
IEEE Pervasive Computing
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Protecting Location Privacy Through Path Confusion
SECURECOMM '05 Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A peer-to-peer spatial cloaking algorithm for anonymous location-based service
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
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
Location anonymity in continuous location-based services
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Private queries in location based services: anonymizers are not necessary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Protecting Privacy in Continuous Location-Tracking Applications
IEEE Security and Privacy
A Linear-Time Multivariate Micro-aggregation for Privacy Protection in Uniform Very Large Data Sets
MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
A survey of computational location privacy
Personal and Ubiquitous Computing
On non-cooperative location privacy: a game-theoretic analysis
Proceedings of the 16th ACM conference on Computer and communications security
Feeling-based location privacy protection for location-based services
Proceedings of the 16th ACM conference on Computer and communications security
Towards an information theoretic metric for anonymity
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
The PROBE Framework for the Personalized Cloaking of Private Locations
Transactions on Data Privacy
On the age of pseudonyms in mobile ad hoc networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Location privacy and resilience in wireless sensor networks querying
Computer Communications
Event handoff unobservability in WSN
iNetSec'10 Proceedings of the 2010 IFIP WG 11.4 international conference on Open research problems in network security
Protecting privacy against location-based personal identification
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
Limits to anonymity when using credentials
SP'04 Proceedings of the 12th international conference on Security Protocols
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Location Based Services (LBSs) introduce several privacy issues, the most relevant ones being: (i) how to anonymize a user; (ii) how to specify the level of anonymity; and, (iii) how to guarantee to a given user the same level of desired anonymity for all of his requests. Anonymizing the user within k potential users is a common solution to (i). A recent work [28] highlighted how specifying a practical value of k could be a difficult choice for the user, hence introducing a feeling based model: a user defines the desired level of anonymity specifying a given area (e.g. a shopping mall). The proposal sets the level of anonymity (ii) as the popularity of the area--popularity being measured via the entropy of the footprints of the visitors in that area. To keep the privacy level constant (iii), the proposal conceals the user requests always within an area of the same popularity--independently from the current user's position. The main contribution of this work is to highlight the importance of the time when providing privacy in LBSs. Further, we show how applying our considerations user privacy can be violated in the related model, but also in a relaxed one. We support our claim with both analysis and a practical counter-example.