  Multi-party machine learning is a paradigm in which multiple participants
collaboratively train a machine learning model to achieve a common learning
objective without sharing their privately owned data. The paradigm has recently
received a lot of attention from the research community aimed at addressing its
associated privacy concerns. In this work, we focus on addressing the concerns
of data privacy, model privacy, and data quality associated with
privacy-preserving multi-party machine learning, i.e., we present a scheme for
privacy-preserving collaborative learning that checks the participants' data
quality while guaranteeing data and model privacy. In particular, we propose a
novel metric called weight similarity that is securely computed and used to
check whether a participant can be categorized as a reliable participant (holds
good quality data) or not. The problems of model and data privacy are tackled
by integrating homomorphic encryption in our scheme and uploading encrypted
weights, which prevent leakages to the server and malicious participants,
respectively. The analytical and experimental evaluations of our scheme
demonstrate that it is accurate and ensures data and model privacy.
