  To develop Smart City, the growing popularity of Machine Learning (ML) that
appreciates high-quality training datasets generated from diverse IoT devices
raises natural questions about the privacy guarantees that can be provided in
such settings. Privacy-preserving ML training in an aggregation scenario
enables a model demander to securely train ML models with the sensitive IoT
data gathered from personal IoT devices. Existing solutions are generally
server-aided, cannot deal with the collusion threat between the servers or
between the servers and data owners, and do not match the delicate environments
of IoT. We propose a privacy-preserving ML training framework named Heda that
consists of a library of building blocks based on partial homomorphic
encryption (PHE) enabling constructing multiple privacy-preserving ML training
protocols for the aggregation scenario without the assistance of untrusted
servers and defending the security under collusion situations. Rigorous
security analysis demonstrates the proposed protocols can protect the privacy
of each participant in the honest-but-curious model and defend the security
under most collusion situations. Extensive experiments validate the efficiency
of Heda which achieves the privacy-preserving ML training without losing the
model accuracy.
