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llm-jp-membership-inference

This is the repository for llm jp membership inference attack.

Description

Membership inference attack is a type of privacy attack that aims to determine whether a specific data sample was used to train a machine learning model.
In this project, we implement current representative membership inference attacks.
For a specific method, usually, it caculates a feature value for every input sample. Then each MIA method has its own hypothesis in how to seperate trained or un-trained samples, for exmaple, the loss method would assume that the loss of trained samples is smaller than un-trained samples. Then it would use the feature value to predict the label of the sample.

Attack Methods

We implement the following MIA methods:

  • Loss-based MIA
  • Gradient-based MIA
  • Perplexity-based MIA
  • RECALL-based MIA

How to run the code

Please refer to the test.py file for how to run the code.

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This is the repository for llm jp membership inference attack.

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