Papers by Yunlong Duan

2 papers
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)

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Challenge: Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs).
Approach: They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models.
Outcome: The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks.
MdEval: Massively Multilingual Code Debugging (2026.findings-acl)

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Challenge: Existing benchmarks primarily focus on Python and are limited in terms of language diversity.
Approach: They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions.
Outcome: The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task.

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