Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Differentially private aggregation of distributed time-series with transformation and encryption
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
SEPIA: privacy-preserving aggregation of multi-domain network events and statistics
USENIX Security'10 Proceedings of the 19th USENIX conference on Security
Privacy and accountability for location-based aggregate statistics
Proceedings of the 18th ACM conference on Computer and communications security
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Extracting significant places from mobile user GPS trajectories: a bearing change based approach
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Differential private trajectory protection of moving objects
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
Differentially private multi-dimensional time series release for traffic monitoring
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
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We consider mobile applications that let users learn traffic conditions based on reports from other users. However, the providers of these mobile services have access to such sensitive information as timestamped locations and movements of its users. In this paper, we introduce the model and general approach of Haze, a system for traffic-update applications that supports the creation of traffic statistics from user reports while protecting the privacy of the users. We also present preliminary experiments that indicate potential for a practical deployment of Haze.