# MLSec

**Repository Path**: qi_qi_sha/MLSec

## Basic Information

- **Project Name**: MLSec
- **Description**: It's a repository to implement some experiments on Machine Learning Security
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-11-25
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MLSec
It's a repository to implement some experiments on Machine Learning Security
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## Code Description

### 1. Data_Poisoning Directory

- label_poisoning_attack_svm.py

It aims to implement a data poisoning attack on labels with SVM classifier.


### 2. Membership_Inference Directory

The code under this directory is implementation of 17 S&P paper "Membership Inference Attacks Against Machine Learning Models" by Shokri et al.

- data_synthesis.py

It aims to implement `Algorithm 1: Data Synthesis` in paper.

- shadow_models.py

It aims to implement `Shadow model technique` in paper.

- attack_models.py

It aims to implement `Membership Inference Attack` according to paper.


#### Limitation

- I have only implemented `Membership Inference Attack` with classifier `RandomForest` in Python3 package `sklearn`. In the future, I will add various classifiers, like `Neural Network` and so on.

- In my code, I just implement the experiments. However, the code should be wrapped in a `class` for reusing. 

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## Reference
### 1. Paper

- [17 S&P] Membership Inference Attacks Against Machine Learning Models

- [15 IJSN Attribute Infenrence Attack] Hacking Smart Machines with Smarter Ones How to Extract Meaningful Data from Machine Learning Classifiers


### 2. Code

**Membership Inference Attack**: 

- Paper code: https://github.com/csong27/membership-inference

- BielStela's experiment implementation: https://github.com/BielStela/membership_inference