Privacy-Preserving Machine Learning Training in Aggregation Scenarios
This work addresses privacy concerns for IoT data owners in smart city applications, offering a novel solution to server-aided limitations, though it is incremental in building upon existing encryption techniques.
The authors tackled the problem of privacy-preserving machine learning training in IoT aggregation scenarios by proposing Heda, a framework based on partial homomorphic encryption that eliminates the need for untrusted servers and defends against collusion threats, achieving this without compromising model accuracy.
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.