Personalised Privacy-Preserving Network Data Publishing System

数据共享已成为推动工业部门许多企业发展的动力。该项目旨在开发一种隐私保护网络数据发布系统，该系统可以以个性化的方式保护用户隐私，同时保持已发布数据的最大效用。为了进行准确的隐私保护，本项目将设计新的学习模型来获取准确的用户相关性和他们的隐私意图，并开发高效的隐私保护算法来处理静态和动态的网络数据共享。该项目的成功将使许多行业和政府机构受益，以减少用户的隐私侵犯，避免共享数据的非法后果，并提高这些服务提供商的服务质量。

Data sharing has become a driving force for many businesses in industrial sectors. This project aims to develop a privacy preserving network data publishing system that can preserve user privacy in a personalised way while maintaining maximal utility of the published data. To make accurate privacy preservation, this project will design novel learning models to derive accurate users’ correlation and their privacy intention, develop efficient privacy preserving algorithms to deal with static and dynamic network data sharing. The success of this project will benefit many industries and government agencies to reduce users’ privacy breaches, avoid illegal consequences of sharing data, and enhance these service providers’ service quality.