Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
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
Anonymizing moving objects: how to hide a MOB in a crowd?
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Towards Trajectory Anonymization: a Generalization-Based Approach
Transactions on Data Privacy
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Communications of the ACM
Movement Data Anonymity through Generalization
Transactions on Data Privacy
Data mining with differential privacy
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymization of moving objects databases by clustering and perturbation
Information Systems
A firm foundation for private data analysis
Communications of the ACM
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques
Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques
Evaluating Laplace Noise Addition to Satisfy Differential Privacy for Numeric Data
Transactions on Data Privacy
Differentially Private Empirical Risk Minimization
The Journal of Machine Learning Research
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Mining frequent graph patterns with differential privacy
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Geo-indistinguishability: differential privacy for location-based systems
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Privacy vulnerability of published anonymous mobility traces
IEEE/ACM Transactions on Networking (TON)
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One main concern for individuals to participate in the data collection of personal location history records is the disclosure of their location and related information when a user queries for statistical or pattern mining results derived from these records. In this paper, we investigate how the privacy goal that the inclusion of one's location history in a statistical database with location pattern mining capabilities does not substantially increase one's privacy risk. In particular, we propose a differentially private pattern mining algorithm for interesting geographic location discovery using a region quadtree spatial decomposition to preprocess the location points followed by applying a density-based clustering algorithm. A differentially private region quadtree is used for both de-noising the spatial domain and identifying the likely geographic regions containing the interesting locations. Then, a differential privacy mechanism is applied to the algorithm outputs, namely: the interesting regions and their corresponding stay point counts. The quadtree spatial decomposition enables one to obtain a localized reduced sensitivity to achieve the differential privacy goal and accurate outputs. Experimental results on synthetic datasets are used to show the feasibility of the proposed privacy preserving location pattern mining algorithm.