|Position||Visiting Scholar/PhD student|
Privacy in Mobile Computing
We tackle the location privacy problem in a predication way by recommending a privacy-preserving path for a requester. We consider the popular navigation application, where users may continuously query different location based servers during their movements. Based on a set of metrics on privacy, distance and the quality of services that a LBS requester often desires, a secure path is computed for each request according to user’s preference, and can be dynamically adjusted when the situation is changed.
Incentive Mechanism in Crowdsourcing
In open crowdsourcing applications, workers are often with diverse capacities, various initiatives and without restrictions, which highly influence the process of a problem being solved. So, an incentive mechanism is desired so as to promote the correct results of problems. We propose an incentive mechanism to reward those active and excellent users, which takes into account the quality and quantity of answers a user provided, as well as the problems a user solved. The performance of this incentive is evaluated on tagging systems, which also can be applied to other crowdsourcing systems.
User Preference Analysis and Personalized Privacy Protection