Paper 2024/662

Faster Private Decision Tree Evaluation for Batched Input from Homomorphic Encryption

Abstract

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 useful in privacy-preserving credit scoring, biometric authentication, and many more applications. In this paper, we propose two novel non-interactive batched PDTE protocols, OursRCC and OursCW, based on two batched ciphertext-plaintext comparison algorithms, our batched range cover comparison (RCC) comparator and the constant-weight (CW) piece-wise comparator, respectively. When comparing 16-bit batched encrypted values to a single plaintext value, our comparison algorithms show a speedup of up to $72\times$ compared to the state-of-the-art Level Up (CCS'23). Moreover, we introduced a new tree traversal method called adapted SumPath, to achieve $\mathcal{O}(1)$ complexity of the server's response, whereas Level Up has $\mathcal{O}(2^d)$ complexity for a depth-$d$ tree and the client needs to look up classification values in a table. Overall, our PDTE protocols attain the optimal server-to-client communication complexity and are up to $17\times$ faster than Level Up in batch size 16384.