Privacy-Aware Service Subscription in People-Centric Sensing: A Combinatorial Auction Approach

摘要

With the emergence of ambient sensing technologies which combine mobile crowdsensing and Internet of Things, large amount of people-centric data can be obtained and utilized to build people-centric services. Note that the service quality is highly related to the privacy level of the data. In this paper, we investigate the problem of privacy-aware service subscription in people-centric sensing. An efficient resource allocation framework using a combinatorial auction (CA) model is provided. Specifically, the resource allocation problem that maximizes the social welfare in view of varying requirements of multiple users is formulated, and it is solved by a proposed computationally tractable solution algorithm. Furthermore, the prices of allocated resources that winners need to pay are figured out by a designed scheme. Numerical results demonstrate the effectiveness of the proposed scheme.