Cryptology ePrint Archive: Report 2020/1181

TinyGarble2: Smart, Efficient, and Scalable Yao’s Garble Circuit

Siam Hussain and Baiyu Li and Farinaz Koushanfar and Rosario Cammarota

Abstract: We present TinyGarble2 – a C++ framework for privacy-preserving computation through the Yao’s Garbled Circuit (GC) protocol in both the honest-but-curious and the malicious security models. TinyGarble2 provides a rich library with arithmetic and logic building blocks for developing GC-based secure applications. The framework offers abstractions among three layers: the C++ program, the GC back-end and the Boolean logic representation of the function being computed. TinyGarble2 thus allowing the most optimized versions of all pertinent components. These abstractions, coupled with secure share transfer among the functions make TinyGarble2 the fastest and most memory-efficient GC framework. In addition, the framework provides a library for Convolutional Neural Networks (CNN). Our evaluations show that TinyGarble2 is the fastest among the current end-to-end GC frameworks while also being scalable in terms of memory footprint. Moreover, it performs 18× faster on the CNN LeNet-5 compared to the existing scalable frameworks.

Category / Keywords: implementation / Privacy, Secure Multi-Party Computation (MPC), Secure Function Evaluation (SFE), Yao's Garbled Circuit (GC), Secure Neural Network Inference

Original Publication (in the same form): 2020 ACM Workshop on Privacy-Preserving Machine Learning in Practice (PPMLP'20)

Date: received 26 Sep 2020

Contact author: siamumar at ucsd edu

Available format(s): PDF | BibTeX Citation

Version: 20200930:074106 (All versions of this report)

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