Automated Verifier for Black-box Local Differential Privacy Algorithms

The objective of this project was to develop an automated verifier for black-box Local Differential Privacy (LDP) algorithms. You can run 4 different experiments with this project.

Proposed Appoach

1. Testing for Convergence in Terms of Unbiased Estimation
2. Violation Detection Through p-value Test

Algorithms

RR, GR, Simple RAPPOR and OUE algorithms can be found in LDP_Algorithms directory. If you want to add another algortihm, it should be a subclass of LDP_Base class.

Run Verifier locally

Step 1: Clone the project

git clone https://github.com/alikaratas17/COMP-430-Project.git
cd COMP-430-Project

Step 2: Install needed packages if they are not exist

sudo apt install -r requirements.txt

Step 3: Run the project using the following command

main.py [-h] -a ALGO -e EPSILON -m MODE [-n ITERATION]

ALGO is the name of the algorithm such as RR, GRR, OUE, SimpleRAPPOR. EPSILON is the epsilon value for the algorithm. MODE is an integer value corresponding to the type of the experiment (1: Convergence, 2: Direct Epsilon Estimation, 3: P-value Estimation, 4: P-value Plot). ITERATION is an optional argument that corresponds to the number of iterations.

Optional: Add custom algorithms

If you want to check a custom algorithm, you need to create a class inherits from LDP_Base, then add it to the LDP_Algorithms folder. Make sure the algorithm name in arguments and the name of the python file are the same when you run the code with command line.