  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. We present a
novel privacy-respecting framework for distributed graph learning and
graph-based machine learning. In order to perform graph learning and other
downstream tasks on the server side, this framework aims to learn features as
well as distances without requiring actual features while preserving the
original structural properties of the raw data. The proposed framework is quite
generic and highly adaptable. We demonstrate the utility of the Euclidean
space, but it can be applied with any existing method of distance approximation
and graph learning for the relevant spaces. Through extensive experimentation
on both synthetic and real datasets, we demonstrate the efficacy of the
framework in terms of comparing the results obtained without data sharing to
those obtained with data sharing as a benchmark. This is, to our knowledge, the
first privacy-preserving distributed graph learning framework.
