GPDP: Game-Enhanced Personalized Differentially Private Smart Community

Abstract

Smart community, as a key component of Internet of Things (IoT), is experiencing fast booming, which significantly enhances residents' quality of life. Using fog computing to establish a smart community is an effective way to further develop smart and sustainable cities due to the reduction of bandwidth consumption and latency. However, privacy issues are emerging because the continuous attacks are putting sensitive information under great threats. Motivated by this, we propose a differentially private smart community model with personalized privacy protection. Differential privacy is deployed in a personalized way while a logarithmic function is leveraged to map data sensitivity to privacy protection level. Extensive evaluation results show the advantages of the proposed model from the perspective of the optimized trade-off between personalized privacy protection and data utility.