Summary of Privacy Evaluation Benchmarks For Nlp Models, by Wei Huang et al.
Privacy Evaluation Benchmarks for NLP Models
by Wei Huang, Yinggui Wang, Cen Chen
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a comprehensive benchmark for evaluating privacy risks in Natural Language Processing (NLP) models. The authors highlight the importance of considering various scenarios, factors, and relationships between different attacks to understand their impact on NLP models. They present a benchmark that supports multiple model types, datasets, and protocols, along with standardized modules for evaluating attacks and defense strategies. The paper also explores the association between auxiliary data from different domains and the strength of privacy attacks, proposing an improved attack method using Knowledge Distillation (KD). Additionally, it introduces a chained framework for privacy attacks, enabling practitioners to combine multiple attacks to achieve higher-level objectives. The authors provide code for reproducing results at GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to measure the risk of data being stolen from Natural Language Processing models. Right now, there are no standardized ways to do this. The authors want to change that by creating a benchmark that shows how well different attacks work on NLP models and what defenses can be used against them. They also explore how using extra information from other domains can make some attacks stronger or weaker. The paper includes an improved way of attacking and defending, as well as a new approach to combining multiple attacks for even bigger effects. |
Keywords
» Artificial intelligence » Knowledge distillation » Natural language processing » Nlp
