Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Cryptanalytic Attacks on Pseudorandom Number Generators
FSE '98 Proceedings of the 5th International Workshop on Fast Software Encryption
Modeling mobility for vehicular ad-hoc networks
Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
CarTel: a distributed mobile sensor computing system
Proceedings of the 4th international conference on Embedded networked sensor systems
Vehicular Mobility Simulation for VANETs
ANSS '07 Proceedings of the 40th Annual Simulation Symposium
The BikeNet mobile sensing system for cyclist experience mapping
Proceedings of the 5th international conference on Embedded networked sensor systems
PoolView: stream privacy for grassroots participatory sensing
Proceedings of the 6th ACM conference on Embedded network sensor systems
Proceedings of the 6th ACM conference on Embedded network sensor systems
GreenGPS: a participatory sensing fuel-efficient maps application
Proceedings of the 8th international conference on Mobile systems, applications, and services
Privacy-aware regression modeling of participatory sensing data
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Review: From wireless sensor networks towards cyber physical systems
Pervasive and Mobile Computing
Cloud-enabled privacy-preserving collaborative learning for mobile sensing
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Maximum likelihood analysis of conflicting observations in social sensing
ACM Transactions on Sensor Networks (TOSN)
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The proliferation of sensors in devices of frequent use, such as mobile phones, offers unprecedented opportunities for forming self-selected communities around shared sensory data pools that enable community specific applications of mutual interest. Such applications have recently been termed participatory sensing. An important category of participatory sensing applications is one that construct maps of different phenomena (e.g., traffic speed, pollution) using vehicular participatory sensing. An example is sharing data from GPS-enabled cell-phones to map traffic or noise patterns. Concerns with data privacy are a key impediment to the proliferation of such applications. This paper presents theoretical foundations, a system implementation, and an experimental evaluation of a perturbation-based mechanism for ensuring privacy of location-tagged participatory sensing data while allowing correct reconstruction of community statistics of interest (computed from shared perturbed data). The system is applied to construct accurate traffic speed maps in a small campus town from shared GPS data of participating vehicles, where the individual vehicles are allowed to “lie” about their actual location and speed at all times. An extensive evaluation demonstrates the efficacy of the approach in concealing multi-dimensional, correlated, time-series data while allowing for accurate reconstruction of spatial statistics.