BPDTE: Batch Private Decision Tree Evaluation via Amortized Efficient Private Comparison

ePrint Report: BPDTE: Batch Private Decision Tree Evaluation via Amortized Efficient Private Comparison

Huiqiang Liang, Haining Lu, Geng Wang

Machine learning as a service requires the client to trust the server and provide its own private information to use this service. Usually, clients may worry that their private data is being collected by server without effective supervision, the server aims to ensure proper management of the user data, thereby fostering the advancement of its services. In this work, we focus …

batch client clients data decision eprint report evaluation information machine machine learning may own private private data report server service trust wang