Secure two-party computation is a rapidly emerging field of research and enables a large variety of privacy-preserving applications such as mobile social networks or biometric identification. In the late eighties, two different approaches were proposed: Yao's garbled circuits and the protocol of Goldreich-Micali-Wigderson (GMW). Since then, research has mostly focused on Yao's garbled circuits as they were believed to yield better efficiency due to their constant round complexity. In this work we give several optimizations for an efficient implementation of the GMW protocol. We show that for semi-honest adversaries the optimized GMW protocol can outperform today's most efficient implementations of Yao's garbled circuits, but highly depends on a low network latency. As a first step to overcome these latency issues, we summarize depth-optimized circuit constructions for various standard tasks. As application scenario we consider privacy-preserving face recognition and show that our optimized framework is up to 100 times faster than previous works even in settings with high network latency.
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