Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System? (2020.tacl-1)
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| Challenge: | Data privacy is an important issue for “machine learning as a service” providers. |
| Approach: | They propose an attack on membership inference attacks using a sequence-to-sequence model and a machine translation dataset to investigate the feasibility of a privacy attack. |
| Outcome: | The proposed model can infer sentence-level membership from the output of the model, but it is difficult to infer it. |
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| Challenge: | Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks. |
| Approach: | They investigate the membership inference attack by using model's API to determine if a sample was part of the training data. |
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| Challenge: | Prior attempts at measuring leakage of MLMs via membership inference attacks have been inconclusive, implying potential robustness of Mlms to privacy attacks. |
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| Challenge: | Membership Inference Attack (MIA) is a method that differentiates trained (member) and untrained (non-member) data. |
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| Challenge: | Membership inference attacks (MIAs) attempt to verify the membership of a data sample in the training set for a model. |
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| Challenge: | Membership inference attacks are a canonical way to assess a machine learning model’s privacy properties. |
| Approach: | They propose a framework for principled evaluation of membership inference attacks against large language models by leveraging the insight that training data before and after a fixed point during training are drawn from the same distribution. |
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Yuanhong Huang, Huili Wang, Xueying Bai, Jinrui Wang, Jiajun Liu, Ziqin Wang, Wanchun Ni, Shangguang Wang, Tao Qi
| Challenge: | Membership inference attacks are a promising tool for auditing training data of LLMs . existing methods rely on the assumption that LLM's assign higher confidence scores to training samples than to non-training ones. |
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Membership Inference Attacks against Language Models via Neighbourhood Comparison (2023.findings-acl)
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Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schoelkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick
| Challenge: | Existing membership inference attacks aim to predict whether a data sample was present in training data of a machine learning model. |
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