Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Randomization in privacy preserving data mining
ACM SIGKDD Explorations Newsletter
IEEE Pervasive Computing
The BikeNet mobile sensing system for cyclist experience mapping
Proceedings of the 5th international conference on Embedded networked sensor systems
Enabling group-awareness through context-based service provisioning
Proceedings of the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems
Trustworthiness analysis of sensor data in cyber-physical systems
Journal of Computer and System Sciences
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Web-based social networks such as LinkedIn, FaceBook and MySpace have gained wide popularity in recent years. With the advent of ubiquitous sensing, future social networks will be cyber-physical, combining measured elements of the physical world with manual human input. Microsoft SensorMap is an early example of a cyber-physical network in that it allows users to browse the physical world. In contrast to social networks, however, the main abstractions exported by SensorMap are those of physical objects (such as sensors), not people. This work extends the concept of SensorMap to incorporate social entities such as individuals and special-interest communities to which they belong. In the extended network, called SenseWorld, SensorMap offers a geographical index into a cyber-physical social network with its own logical topology derived from social connections.