Free Lunch for Privacy Preserving Distributed Graph Learning. (arXiv:2305.10869v1 [cs.LG])

Learning on graphs is becoming prevalent in a wide range of applications
including social networks, robotics, communication, medicine, etc. These
datasets belonging to entities often contain critical private information. The
utilization of data for graph learning applications is hampered by the growing
privacy concerns from users on data sharing. Existing privacy-preserving
methods pre-process the data to extract user-side features, and only these
features are used for subsequent learning. Unfortunately, these methods are
vulnerable to adversarial attacks to infer private attributes. …

applications communication critical data datasets data sharing distributed entities etc free graphs information medicine networks privacy privacy preserving private robotics sharing social social networks