End-to-end Privacy Preserving Training and Inference for Air Pollution Forecasting with Data from Rival Fleets

ePrint Report: End-to-end Privacy Preserving Training and Inference for Air Pollution Forecasting with Data from Rival Fleets

Gauri Gupta, Krithika Ramesh, Anwesh Bhattacharya, Divya Gupta, Rahul Sharma, Nishanth Chandran, Rijurekha Sen

Privacy-preserving machine learning (PPML) promises to train
machine learning (ML) models by combining data spread across
multiple data silos. Theoretically, secure multiparty computation
(MPC) allows multiple data owners to train models on their joint
data without revealing the data to each other. However, the prior
implementations of this secure …

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