Faster Private Decision Tree Evaluation for Batched Input from Homomorphic Encryption

ePrint Report: Faster Private Decision Tree Evaluation for Batched Input from Homomorphic Encryption

Kelong Cong, Jiayi Kang, Georgio Nicolas, Jeongeun Park

Privacy-preserving decision tree evaluation (PDTE) allows a client that holds feature vectors to perform inferences against a decision tree model on the server side without revealing feature vectors to the server. Our work focuses on the non-interactive batched setting where the client sends a batch of encrypted feature vectors and then obtains classifications, without any additional interaction. This is …

client decision encryption eprint report evaluation feature homomorphic encryption input privacy private report server server side