APDP: Attribute-Based Personalized Differential Privacy Data Publishing Scheme for Social Networks
Mingyue Zhang, Junlong Zhou, Gongxuan Zhang, Lei Cui, Tian Gao, Shui Yu
Abstract
In the Big Data era, the wide usage of mobile devices has led to large amounts of information release and sharing through social networks, where sensitive information of the data owners may be leaked. Traditional approaches that provide the identical privacy protection levels for all users result in poor quality of service, thus the concept of personalized privacy has been proposed in recent years. However, existing methods that add different noises to each user will require both high real-time performance and resource consumption. This paper presents a fine-grained personalized differential privacy data publishing scheme (APDP) for social networks. Specifically, we design a new mechanism that defines the privacy protection levels of different users based on their attribute values. In particular, we exploit the TOPSIS method to map the attribute values to the amount of noise required to add. Furthermore, to prevent illegal download of data, the access control is integrated with differential privacy. Compared with traditional attribute-based encryption data publishing schemes, our scheme can get rid of the expensive computation overhead. Theoretical analyses and simulations show that APDP can realize efficient personalized differential privacy data publishing with reasonable data utility.