In Intelligent Transportation System, GPS has become a major source of floating car data. GPS measurement from vehicles can be collected and be further analyzed for real-time urban traffic sensing or monitoring. However, the main challenge coming to the real-time traffic estimation system is how to scale up the system easily when the number of GPS vehicle probes grows dramatically. In this paper, a scalable real-time traffic estimation system based on distributed stream processing is developed. Differing from the traditional batch- style distributed computing techniques, e.g. MapReduce, the distributed stream processing focus on not only distributed computing but also real- time and in-memory computing such that latency is reduced. We show the system architecture, data flow and some implementation experiences for estimating urban traffic using Twitter Storm, which is the open source distributed stream processing framework. The experiment results illustrate that our system can scale well and scale up easily as the input GPS data increase. It is effective and efficient for applying stream computing in real- time traffic estimation system.

Social networks model the social activities between individuals, which change as time goes by. In light of useful information from such dynamic networks, there is a continuous demand for privacy-preserving data sharing with analyzers, collaborators or customers. In this paper, we address the privacy risks of identity disclosures in sequential releases of a dynamic network. To prevent privacy breaches, we proposed novel kw-structural diversity anonymity, where k is an appreciated privacy level and w is a time period that an adversary can monitor a victim to collect the attack knowledge. We also present a heuristic algorithm for generating releases satisfying kw-structural diversity anonymity so that the adversary cannot utilize his knowledge to reidentify the victim and take advantages. The evaluations on both real and synthetic data sets show that the proposed algorithm can retain much of the characteristics of the networks while confirming the privacy protection.

Privacy in social network data publishing is always an important concern. Nowadays most prior privacy protection techniques focus on static social networks. However, there are additional privacy disclosures in dynamic social networks due to the sequential publications. In this paper, we first show that the risks of vertex and community re-identification exist in a dynamic social network, even if the release at each time instance is protected by a static anonymity scheme. To prevent vertex and community re-identification in a dynamic social network, we propose novel dynamic kw-structural diversity anonymity, where w is the time that an adversary can monitor a victim. This scheme extends the k-structural diversity anonymity to a dynamic scenario. We also present a heuristic to anonymize the releases of networks to satisfy the proposed privacy scheme. The evaluations show that our approach can retain much of the characteristics of the networks while confirming the privacy protection.