Papers by Huseyin Inan
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks (2023.findings-emnlp)
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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. |
| Outcome: | The proposed model is able to identify if a sample was part of the training data and exploits its similarity and resistance to document modifications as potential MI signals on widely used datasets. |
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe (2023.acl-long)
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Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, Robert Sim
| Challenge: | Privacy concerns have increased in data-driven products due to the tendency of machine learning models to memorize sensitive training data. |
| Approach: | They propose a method for generating useful synthetic text with a formal privacy guarantee by fine-tuning a pretrained generative language model with DP. |
| Outcome: | The proposed method produces synthetic text competitive in terms of utility with its non-private counterpart, while providing strong protection against potential privacy leakages. |
Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels (2021.naacl-main)
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Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
| Challenge: | Neural language models have a high capacity for memorization of training samples . however, this can cause privacy degradation and disparate impact on subgroups of users . |
| Approach: | They propose two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy. |
| Outcome: | The proposed methods have favorable utility-privacy trade-off, faster training and uniform treatment of under-represented subgroups. |