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Poster presented at ACM SIGCOMM 2000, Stockholm, Sweden (http://conferences.sigcomm.org/sigcomm/2000/)

We present the CRAWDAD wireless network data archive, the largest archive of its type. CRAWDAD hosts 115 wireless network datasets and tools, many of which are of use, and have been used, for wireless network research. CRAWDAD datasets should be of interest to the WiSec community, and similarly that the community might be interested in contributing data to the archive. Postprint

International audience

Location-Based Services have become increasingly popular due to the prevalence of smart devices. The protection of users’ location privacy in such systems is a vital issue. Conventional privacy protection methods such as manually predefining privacy rules or asking users to make decisions every time they enter a new location may not be usable, and so researchers have explored the use of machine learning to predict preferences. Model-based machine learning classifiers which are used for prediction may be too computationally complex to be used in real-world applications. We propose a location-privacy recommender that can provide users with recommendations of appropriate location privacy settings through user-user collaborative filtering. We test our scheme on real world dataset and the experiment results show that the performance of our scheme is close to the best performance of model-based classifiers and it outperforms model-based classifiers when there are no sufficient training data. Postprint