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 priv...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.展开更多
基金Supported by Program for Changjiang Scholars and Innovative Research Team in University of China (IRT1078)the Key Program of NSFC-Guangdong Union Foundation of China (U1135002)+1 种基金the Major national S&T program of China (2011ZX03005-002)the Fundamental Research Funds for the Central Universities of China (JY10000903001)
文摘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.
