Privacy-Aware Controllable Compressed Data Publishing Against Sparse Estimation Attack in IoT
Mengdi Wang, Di Xiao, Yanping Xiang, Hui Wang · IEEE Internet of Things Journal · 2019
The openness and inclusiveness of Internet of Things (IoT) inevitably result in security risks. Privacy protection in data publishing in IoT has become one of the most important topics. For data owners, besides the privacy of released information, they are increasingly concerned about the privacy of unreleased data because data users are particularly keen to analyze results from the released information. Our experimental results demonstrate that a small number of known data could be successfully utilized to estimate the unknowns. To address the unreleased data privacy while guaranteeing the utility of released data, we propose a privacy-aware controllable compressed data publishing strategy that could resist sparse estimation attacks efficiently. Specifically, by introducing compressive sensing (CS) technology to achieve compressed data publishing, communication overhead is reduced greatly. With the help of CS reconstruction error, the privacy protection capability of unreleased data is enhanced under the premise of the utility of released data. What is more, users are divided into the data user and the authorized user to realize controllability of the data owner. For the same compression data, the data user with general permissions is limited to the released data from the data owner; while the authorized user is entitled to access the whole data. Furthermore, we analyze the upper bound of the released data number that could satisfy the privacy requirement of the unreleased data. The experimental results demonstrate that the utility of the released data, the privacy of the unreleased data, and the compressibility all achieve desirable effect.