Welcome to the resource topic for 2024/619
Title:
BPDTE: Batch Private Decision Tree Evaluation via Amortized Efficient Private Comparison
Authors: Huiqiang Liang, Haining Lu, Geng Wang
Abstract: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 on private decision tree evaluation (PDTE) which can alleviates the privacy concerns associated with classification tasks using decision tree. After the evaluation, except for some hyperparameters, the client only receives the classification results of their private data from server, while the server learns nothing.
Firstly, we propose three amortized efficient private comparison algorithms: TECMP, RDCMP and CDCMP, which are based on leveled homomorphic encryption. They are non-interactive, high precision (up to 26624-bit), many-to-many, and output expressive, achieving an amortized cost of less than 1 ms under 32-bit, which is an order of magnitude faster than the state-of-the-art. Secondly, we propose three batch PDTE schemes using our private comparison: TECMP-PDTE, RDCMP-PDTE and CDCMP-PDTE. Due to the batch operations, we utilized a clear rows relation (CRR) algorithm, which obfuscates the position and classification results of the different row data. Finally, in decision tree exceeding 1000 nodes with 16-bit each, the amortized runtime of TECMP-PDTE and RDCMP-PDTE both more than 56$\times$ faster than state-of-the-art, while the TECMP-PDTE with CRR still achieves 14$\times$ speedup. Even in a single row and a tree of fewer than 100 nodes with 64-bit, and the TECMP-PDTE maintains a comparable performance with the current work.
ePrint: https://eprint.iacr.org/2024/619
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