A High-Utility Differentially Private Mechanism for Space Information Networks - Ming Zhuo; Wen Huang; Leyuan Liu; Shijie Zhou; Zhiwen Tian (Remote Sensing, 2022)
Nowadays, Space Information Networks represented by the satellite internet are developing rapidly. For example, the Starlink of SpaceX plans to provide network access services worldwide and has drawn much attention. To operate and maintain Space Information Networks, e.g., performing collision avoidance maneuvers and forensic investigation, statistic information on networks such as the average of orbital inclination needs to be shared with analysts. However, for some particular reasons, such as safety or confidentiality, accurate information on networks cannot be shared with analysts. To solve this contradiction, we design a differentially private mechanism for the Space Information Network so that the entities of a network can keep accurate information privacy while sharing statistic information. In particular, we extend differentially private mechanisms based on personalized sampling to distributed communication systems such as Space Information Networks. In comparison with other mechanisms, the proposed mechanism has better data utility. Moreover, the proposed mechanism has a hierarchical privacy guarantee. In particular, there are differences between the privacy guarantees made between system entities and between the system and the user.