This paper introduces results of a study into the value of location privacy for individuals using mobile devices. We questioned a sample of over 1200 people from five EU countries, and used tools from experimental psychology and economics to extract from them the value they attach to their location data. We compare this value across national groups, gender and technical awareness, but also the perceived difference between academic use and commercial exploitation. We provide some analysis of the self-selection bias of such a study, and look further at the valuation of location data over time using data from another experiment.
We propose a new method for automatic generation of secrecy amplification protocols for wireless sensor networks, utilizing evolutionary algorithms. We were able to rediscover all published protocols for secrecy amplification we are aware of, and found a new protocol that outperforms the existing ones. An alternative construction of secrecy amplification protocols with a comparable fraction of secure links to that of the original "node-oriented" approach was also designed. This new construction exhibits only linear (instead of exponential) increase of necessary messages when the number of communication neighbours grows. This efficient protocol can significantly reduce the sensor battery power consumption because of the decreased message transmission rate. We used a combination of linear genetic programming and a network simulator in this work.
The neighbor-based detection technique explores the principle that sensor nodes situated spatially close to each other tend to have a similar behavior. A node is considered malicious if its behavior significantly differs from its neighbors. This detection technique is localized, unsupervised and adapts to changing network dynamics. Although the technique is promising, it has not been deeply researched in the context of wireless sensor networks yet. In this paper, we present symptoms which can be used in the neighbor-based technique for detection of selective forwarding, jamming and hello flood attacks. We implemented an intrusion detection system which employs the neighbor-based detection technique. The system was designed for and works on the TinyOS operating system running the Collection Tree Protocol. We evaluated accuracy of the technique in the detection of selective forwarding, jamming and hello flood attacks. The results show that the neighborbased detection technique is highly accurate, especially in the case when collaboration among neighboring nodes is used.
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