Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Minimum Spanning Tree Partitioning Algorithm for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
CarTel: a distributed mobile sensor computing system
Proceedings of the 4th international conference on Embedded networked sensor systems
WICON '06 Proceedings of the 2nd annual international workshop on Wireless internet
Proceedings of the 4th workshop on Embedded networked sensors
Efficient and robust pseudonymous authentication in VANET
Proceedings of the fourth ACM international workshop on Vehicular ad hoc networks
The BikeNet mobile sensing system for cyclist experience mapping
Proceedings of the 5th international conference on Embedded networked sensor systems
Virtual trip lines for distributed privacy-preserving traffic monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
Micro-Blog: sharing and querying content through mobile phones and social participation
Proceedings of the 6th international conference on Mobile systems, applications, and services
Anonysense: privacy-aware people-centric sensing
Proceedings of the 6th international conference on Mobile systems, applications, and services
An Improved V-MDAV Algorithm for l-Diversity
ISIP '08 Proceedings of the 2008 International Symposiums on Information Processing
PoolView: stream privacy for grassroots participatory sensing
Proceedings of the 6th ACM conference on Embedded network sensor systems
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Proceedings of the 6th ACM conference on Embedded network sensor systems
Proceedings of the 6th ACM conference on Embedded network sensor systems
A distributed k-anonymity protocol for location privacy
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Microaggregation for database and location privacy
NGITS'06 Proceedings of the 6th international conference on Next Generation Information Technologies and Systems
Short paper: PEPSI---privacy-enhanced participatory sensing infrastructure
Proceedings of the fourth ACM conference on Wireless network security
A survey on privacy in mobile participatory sensing applications
Journal of Systems and Software
Research challenges towards the Future Internet
Computer Communications
Preserving query privacy in urban sensing systems
ICDCN'12 Proceedings of the 13th international conference on Distributed Computing and Networking
Privacy bubbles: user-centered privacy control for mobile content sharing applications
WISTP'12 Proceedings of the 6th IFIP WG 11.2 international conference on Information Security Theory and Practice: security, privacy and trust in computing systems and ambient intelligent ecosystems
Ego network models for Future Internet social networking environments
Computer Communications
Information Security Tech. Report
An efficient and robust privacy protection technique for massive streaming choice-based information
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The ubiquity of mobile devices has brought forth the concept of participatory sensing, whereby ordinary citizens can now contribute and share information from the urban environment. However, such applications introduce a key research challenge: preserving the privacy of the individuals contributing data. In this paper, we study two different privacy concepts, k-anonymity and l-diversity, and demonstrate how their privacy models can be applied to protect users' spatial and temporal privacy in the context of participatory sensing. The first part of the paper focuses on schemes implementing k-anonymity. We propose the use of microaggregation, a technique used for facilitating disclosure control in databases, as an alternate to tessellation, which is the current state-of-the-art for location privacy in participatory sensing applications. We conduct a comparative study of the two techniques and demonstrate that each has its advantage in certain mutually exclusive situations. We then propose the Hybrid Variable size Maximum Distance to Average Vector (Hybrid-VMDAV) algorithm, which combines the positive aspects of microaggregation and tessellation. The second part of the paper addresses the limitations of the k-anonymity privacy model. We employ the principle of l-diversity and propose an l-diverse version of VMDAV (LD-VMDAV) as an improvement. In particular, LD-VMDAV is robust in situations where an adversary may have gained partial knowledge about certain attributes of the victim. We evaluate the performances of our proposed techniques using real-world traces. Our results show that Hybrid-VMDAV improves the percentage of positive identifications made by an application server by up to 100% and decreases the amount of information loss by about 40%. We empirically show that LD-VMDAV always outperforms its k-anonymity counterpart. In particular, it improves the ability of the applications to accurately interpret the anonymized location and time included in user reports. Our studies also confirm that perturbing the true locations of the users with random Gaussian noise can provide an extra layer of protection, while causing little impact on the application performance.