QuickNLP: Faster Protocol of Secure Natural Language Processing for Edge Computing

Artificial intelligence (AI) on edge refers to combining edge computing and AI, and enjoys the benefit of distributed structure, intelligence, and timeliness. Specifically, natural language processing model, which allows to processing language data right close to the device location within milliseconds and providing intelligent controller, have revolutionized researches. Recently, privacy concerns spiked when it is applied in healthcare, autonomous vehicles, manufacturing, etc. Secure multi-party computation has the advantage of strong security guarantee and computability over multi-sourced data. However, it is challenging to translate the timeliness benefit of Edge AI to secure deployment, as only constrained computation and storage resources are available for the edge nodes. We focus on the natural language processing (NLP), and design an efficient secure three-party computation protocol (called QuickNLP) in the semi-honest setting tolerating one corruption. Specifically, for the non-linear operations, we adopt the constant-round distributed comparison function DCF to evaluate the piecewise function efficiently with high accuracy. The proposed framework has been experimented with Python and the results show that QuickNLP could be a valuable solution for data privacy in edge computing. Specifically, compared to the existing protocol, we improve the computation costs by a factor of roughly $7\times$.