Summary of Privacy Risks Of Speculative Decoding in Large Language Models, by Jiankun Wei et al.
Privacy Risks of Speculative Decoding in Large Language Models
by Jiankun Wei, Abdulrahman Abdulrazzag, Tianchen Zhang, Adel Muursepp, Gururaj Saileshwar
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC); 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 The paper investigates the privacy risks of speculative decoding in large language models. It demonstrates that input-dependent patterns of correct and incorrect predictions can be leaked out to an adversary monitoring token generation times and packet sizes, leading to privacy breaches. The authors show that a malicious attacker can fingerprint queries and learn private user inputs with high accuracy across three different speculative decoding techniques. Additionally, the paper highlights how an adversary can leak confidential intellectual property used in these techniques, such as data from data-stores or hyper-parameters. To mitigate these risks, the authors propose strategies like aggregating tokens across multiple iterations and padding packets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are powerful tools that can generate text quickly by guessing what comes next. But researchers have discovered a problem: this fast generation comes at a cost to privacy. When someone uses one of these models, it’s possible for an attacker to figure out what they’re typing just by looking at the way the model works. This is because the model gives away clues about what it thinks might come next. The paper shows that an attacker can use these clues to guess what someone is typing with over 90% accuracy. It also shows how an attacker could get information from confidential sources, like data used to train the model or the settings used to make predictions. |
Keywords
* Artificial intelligence * Token
