Web-based Large Language Model (LLM) services have been widely adopted and
have become an integral part of our Internet experience. Third-party plugins
enhance the functionalities of LLM by enabling access to real-world data and
services. However, the privacy consequences associated with these services and
their third-party plugins are not well understood. Sensitive prompt data are
stored, processed, and shared by cloud-based LLM providers and third-party
plugins. In this paper, we propose Casper, a prompt sanitization technique that
aims to protect user privacy by detecting and removing sensitive information
from user inputs before sending them to LLM services. Casper runs entirely on
the user's device as a browser extension and does not require any changes to
the online LLM services. At the core of Casper is a three-layered sanitization
mechanism consisting of a rule-based filter, a Machine Learning (ML)-based
named entity recognizer, and a browser-based local LLM topic identifier. We
evaluate Casper on a dataset of 4000 synthesized prompts and show that it can
effectively filter out Personal Identifiable Information (PII) and
privacy-sensitive topics with high accuracy, at 98.5% and 89.9%, respectively.
外部データセット
4000 synthesized prompts
1000 synthesized prompts with named entities
1000 prompts without named entities
1000 synthesized prompts (500 with medical topics and 500 with legal topics)
参考文献
Proc. NeurIPS
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, L. Zhang
Published: 2016
arxiv
被引用数 1
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas
Published: 2016.2.18
Modern mobile devices have access to a wealth of data suitable for learning
models, which in turn can greatly improve the user experience on the device.
For example, language models can improve speech recognition and text entry, and
image models can automatically select good photos. However, this rich data is
often privacy sensitive, large in quantity, or both, which may preclude logging
to the data center and training there using conventional approaches. We
advocate an alternative that leaves the training data distributed on the mobile
devices, and learns a shared model by aggregating locally-computed updates. We
term this decentralized approach Federated Learning.
We present a practical method for the federated learning of deep networks
based on iterative model averaging, and conduct an extensive empirical
evaluation, considering five different model architectures and four datasets.
These experiments demonstrate the approach is robust to the unbalanced and
non-IID data distributions that are a defining characteristic of this setting.
Communication costs are the principal constraint, and we show a reduction in
required communication rounds by 10-100x as compared to synchronized stochastic
gradient descent.
