ARXIV:2603.14756 · PRIVACY-PRESERVING NLP · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

arXiv

A novel task and benchmark for privacy-preserving machine translation to protect sensitive information during inference.

Blocked on Code›Score4.0Evidence unverified

Opportunity summary

Pain A novel task and benchmark for privacy-preserving machine translation to protect sensitive information during inference.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

A novel task and benchmark for privacy-preserving machine translation to protect sensitive information during inference. This risk hinders the application of online translation services in privacy-sensitive scenarios.

METHOD

Full 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.

RESULT

ScienceToStartup currently rates this 4.0/10 on the public viability pass. We expect this research work will provide a new perspective and a solid foundation for the privacy protection problem in machine translation.

WHY NOW

Privacy-Preserving NLP moved forward this cycle; last verified April 2026. Public score 4.0/10.

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Opportunity summary

Score4.0

PainA novel task and benchmark for privacy-preserving machine translation to protect sensitive information during inference.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A novel task and benchmark for privacy-preserving machine translation to protect sensitive information during inference.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

A novel task and benchmark for privacy-preserving machine translation to protect sensitive information during inference.

Segment

Privacy-Preserving NLP

Adoption evidence

No public code link in the paper record yet

Commercial read

4.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

A novel task and benchmark for privacy-preserving machine translation to protect sensitive information during inference.

Segment

Privacy-Preserving NLP

Adoption evidence

No public code link in the paper record yet

Commercial read

4.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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

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

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