Towards Privacy-Preserving Machine Translation at the Inference Stage: A New Task and Benchmark

Abstract

Current online translation services require sending user text to cloud servers, posing a risk of privacy leakage when the text contains sensitive information. This risk hinders the application of online translation services in privacy-sensitive scenarios. One way to mitigate this risk for online translation services is introducing privacy protection mechanisms targeting the inference stage of translation models. However, compared to subfields of NLP like text classification and summarization, the machine translation research community has limited exploration of privacy protection during the inference stage. There is no clearly defined privacy protection task for the inference stage, dedicated evaluation datasets and metrics, and reference benchmark methods. The absence of these elements has seriously constrained researchers' in-depth exploration of this direction. To bridge this gap, this paper proposes a novel "Privacy-Preserving Machine Translation" (PPMT) task, aiming to protect the private information in text during the model inference stage. For this task, we constructed three benchmark test datasets, designed corresponding evaluation metrics, and proposed a series of benchmark methods as a starting point for this task. The definition of privacy is complex and diverse. Considering that named entities often contain a large amount of personal privacy and commercial secrets, we have focused our research on protecting only the named entity's privacy in the text. We expect this research work will provide a new perspective and a solid foundation for the privacy protection problem in machine translation.

Figures (5)

• Figure 1: This figure illustrates the workflow of an online translation service. Users are required to transfer the text to be translated from a safe internal environment to the online translation system located in cloud server. The red-colored content ("苹果-Apple", "周五-Friday") in the text represents the privacy information, which constitutes sensitive business information. In sensitive scenarios, users do not want this text, which contains business information, to leave the secure internal environment (typically the company's internal network). However, the current service mode of online translation systems makes it challenging to avoid this issue.
• Figure 2: This is our proposed framework. In this work, we use the client and cloud server to indicate safe internal and risky external environments. The sanitized sentence is sent to the server from the client and the client receive the translation of sanitized text. R1 and R2 represent two different placeholders of two privacy terms "苹果" and "周五" assigned by privacy sanitization module.
• Figure 3: A case of replacement with the dictionary-based placeholder. The privacy parts are in yellow.
• Figure 4: A case of replacement with the tag-based placeholder. The privacy parts are in yellow.
• Figure 5: A case of how to obtain the privacy information's translation according to a phrase translation table.