Tianpei Lu (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Bingsheng Zhang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Xiaoyuan Zhang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Kui Ren (The State Key Laboratory of Blockchain and Data Security, Zhejiang University)

Model quantization has become a common practice in machine learning (ML) to improve efficiency and reduce computational/communicational overhead. However, adopting quantization in privacy-preserving machine learning (PPML) remains challenging due to the complex internal structure of quantized operators, which leads to inefficient protocols under the existing PPML frameworks.

In this work, we propose a new PPML paradigm that is tailor-made for and can benefit from quantized models. Our main observation is that look-up tables can ignore the complex internal constructs of any functions which can be used to simplify the quantized operator evaluation. We view the model inference process as a sequence of quantized operators, and each operator is implemented by a look-up table. We then develop an efficient private look-up table evaluation protocol, and its online communication cost is only $log n$, where $n$ is the size of the look-up table.
On a single CPU core, our protocol can evaluate $2^{26}$ tables with 8-bit input and 8-bit output per second.

The resulting PPML framework for quantized models offers extremely fast online performance.
The experimental results demonstrate that our quantization strategy achieves substantial speedups over SOTA PPML solutions, improving the online performance by $40sim 60 times$ w.r.t. convolutional neural network (CNN) models, such as AlexNet, VGG16, and ResNet18, and by $10sim 25 times$ w.r.t. large language models (LLMs), such as GPT-2, GPT-Neo, and Llama2.

View More Papers

IoT Software Updates: User Perspectives in the Context of...

S. P. Veed, S. M. Daftary, B. Singh, M. Rudra, S. Berhe (University of the Pacific), M. Maynard (Data Independence LLC) F. Khomh (Polytechnique Montreal)

Read More

A Systematic Evaluation of Novel and Existing Cache Side...

Fabian Rauscher (Graz University of Technology), Carina Fiedler (Graz University of Technology), Andreas Kogler (Graz University of Technology), Daniel Gruss (Graz University of Technology)

Read More

Repurposing Neural Networks for Efficient Cryptographic Computation

Xin Jin (The Ohio State University), Shiqing Ma (University of Massachusetts Amherst), Zhiqiang Lin (The Ohio State University)

Read More

Tweezers: A Framework for Security Event Detection via Event...

Jian Cui (Indiana University), Hanna Kim (KAIST), Eugene Jang (S2W Inc.), Dayeon Yim (S2W Inc.), Kicheol Kim (S2W Inc.), Yongjae Lee (S2W Inc.), Jin-Woo Chung (S2W Inc.), Seungwon Shin (KAIST), Xiaojing Liao (Indiana University)

Read More