An anonymous data collection framework for digital community
Hongtao Li, Jianfeng Ma, Shuai Fu · Wuhan University Journal of Natural Sciences · 2013
Collected data in digital community containing sensitive information about individuals or corporations and such information should be protected. In this paper, a security framework based on (a, k)-anonymity for privacy preserving data collection in digital community is proposed. In our framework, aggregation nodes anonymize the collected data to a basic privacy level. Then, the base stations further anonymize the data to a deeper privacy level with encryption-generalizaiton operations. Experimental results and detailed theory analysis demonstrate that this method is effective in terms of privacy levels and data quality with low resource consumption.