Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
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
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
IEEE Transactions on Knowledge and Data Engineering
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Privacy Preservation in the Publication of Trajectories
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Towards trajectory anonymization: a generalization-based approach
SPRINGL '08 Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Location privacy protection through obfuscation-based techniques
Proceedings of the 21st annual IFIP WG 11.3 working conference on Data and applications security
Differentially private aggregation of distributed time-series with transformation and encryption
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Data mining with differential privacy
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Differentially private data release for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A formal model of obfuscation and negotiation for location privacy
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Differentially Private Spatial Decompositions
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Haze: privacy-preserving real-time traffic statistics
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Location privacy and security of spatio-temporal data has come under high scrutiny in the past years. This has rekindled enormous research interest. So far, most of the research studies that attempt to address location privacy are based on the k-Anonymity privacy paradigm. In this paper, we propose a novel technique to ensure location privacy in stream and non-stream mobility data using differential privacy. We portray incoming stream or non-stream mobility data emanating from GPS-enabled devices as a differential privacy problem and rigorously define a spatio-temporal sensitivity function for a trajectory metric space. Privacy is achieved through path perturbation in both the space and time domain. In addition, we introduce a new notion of Nearest Neighbor Anchor Resource to add more contextual meaning in the face of uncertainty to the perturbed trajectory path. Unlike k-Anonymity techniques that require more mobile objects to achieve strong anonymity; we show that our approach provides stronger privacy even for a single moving mobile object, outliers or mobile objects in sparsely populated regions.