Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Differential privacy under continual observation
Proceedings of the forty-second ACM symposium on Theory of computing
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Communications of the ACM
Privacy in inter-vehicular networks: why simple pseudonym change is not enough
WONS'10 Proceedings of the 7th international conference on Wireless on-demand network systems and services
iReduct: differential privacy with reduced relative errors
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
PriPAYD: Privacy-Friendly Pay-As-You-Drive Insurance
IEEE Transactions on Dependable and Secure Computing
Differentially private billing with rebates
IH'11 Proceedings of the 13th international conference on Information hiding
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Towards statistical queries over distributed private user data
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
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In this paper, we investigate how the concept of differential privacy can be applied to Intelligent Transportation Systems (ITS), focusing on protection of Floating Car Data (FCD) stored and processed in central Traffic Data Centers (TDC). We illustrate an integration of differential privacy with privacy policy languages and policy-enforcement frameworks like the PRECIOSA PeRA architecture. Next, we identify differential privacy mechanisms to be integrated within the policy-enforcement framework and provide guidelines for the calibration of parameters to ensure specific privacy guarantees, while still supporting the level of accuracy required for ITS applications. We also discuss the challenges that the support of user-level differential privacy presents and outline a potential solution. As a result, we show that differential privacy could be put to practical use in ITS to enable strong protection of users' personal data.