United We Stand: Accelerating Privacy-Preserving Neural Inference by Conjunctive Optimization with Interleaved Nexus

Document Type

Conference Paper

Publication Date

2024

DOI

10.1609/aaai.v38i15.29620

Publication Title

Proceedings of the AAAI Conference on Artificial Intelligence

Volume

38

Issue

15

Pages

16794-16802

Conference Name

Thirty-Eighth AAAI Conference on Artificial Intelligence, 20-27 February 2024, Vancouver, Canada

Abstract

Privacy-preserving Machine Learning as a Service (MLaaS) enables the powerful cloud server to run its well-trained neural model upon the input from resource-limited client, with both of server's model parameters and client's input data protected. While computation efficiency is critical for the practical implementation of privacy-preserving MLaaS and it is inspiring to witness recent advances towards efficiency improvement, there still exists a significant performance gap to real-world applications. In general, state-of-the-art frameworks perform function-wise efficiency optimization based on specific cryptographic primitives. Although it is logical, such independent optimization for each function makes noticeable amount of expensive operations unremovable and misses the opportunity to further accelerate the performance by jointly considering privacy-preserving computation among adjacent functions. As such, we propose COIN: Conjunctive Optimization with Interleaved Nexus, which remodels mainstream computation for each function to conjunctive counterpart for composite function, with a series of united optimization strategies. Specifically, COIN jointly computes a pair of consecutive nonlinear-linear functions in the neural model by reconstructing the intermediates throughout the whole procedure, which not only eliminates the most expensive crypto operations without invoking extra encryption enabler, but also makes the online crypto complexity independent of filter size. Experimentally, COIN demonstrates 11.2x to 29.6x speedup over various function dimensions from modern networks, and 6.4x to 12x speedup on the total computation time when applied in networks with model input from small-scale CIFAR10 to large-scale ImageNet.

Rights

Copyright © 2024, Association for the Advancement of Artificial Intelligence. All Rights Reserved.

"In the returned rights section of the AAAI copyright form, authors are specifically granted back the right to use their own papers for noncommercial uses, such as inclusion in their dissertations or the right to deposit their own papers in their institutional repositories, provided there is proper attribution."

Included in accordance with publisher policy.

Original Publication Citation

Zhang, Q., Xiang, T., Xin, C., & Wu, H. (2024). United we stand: Accelerating privacy-preserving neural inference by conjunctive optimization with interleaved nexus. Proceedings of the AAAI Conference on Artificial Intelligence, 38(15), 16794-16802. https://doi.org/10.1609/aaai.v38i15.29620

Link to Full Text

Share

COinS