A Collaborative Mechanism for Private Data Publication in Smart Cities

The collection of high-confidence data has been one prominent step for many services in smart city systems. However, the privacy issues have been thwarting the seamless publication of data, especially as the data from different aspects of daily life may provide unprecedented coverage of contributors. Current solutions have been carefully designed to perturb or suppress the data before publication, so as to balance the privacy and utilities. However, they cannot fit the practice in smart cities, where multiple service providers request information on heterogeneous domains and regions of the city. Therefore, this article proposes a novel framework for data publication of workers in smart city systems. The framework allows workers and requestors to own and request various types of contents in different regions. The objective is to maximize the number of service providers receiving qualified utilities under privacy constraints. Furthermore, differential privacy is applied to guarantee that workers will not disclose personal information to requestors. In the technical part, the problem is proved to be NP-complete. Then two algorithms and strategies are proposed toward different cases: 1) workers apply identical privacy budgets for all published data and 2) workers are flexible on privacy settings. Both algorithms are theoretically analyzed on their performance of the released results. Finally, the evaluation of data sets of local businesses reveals that proposed algorithms can outperform baseline methods.