Overcoming Challenges in PLDP: A New Paradigm for Protecting Privacy Budgets and Enhancing Data Utility
- In the file UPRAPPOR, there is our approximate unbiased estimation method code based on the implementation of Basic-Rappor.
- In the file EMalgorithm, there is code for an optimization algorithm based on the implementation of the expectation maximization algorithm
- In the file UPKRR, there is a code for our approximate unbiased estimation method based on the implementation of k-RR.
- In the file samplepower, there is code to generate a power law distribution based on the set of privacy budgets.
The datasets folder has the following (pre-processed) datasets.
- I am slowly cleaning/generalizing the codes + documentation.
I mainly used Python 3 with numpy, pandas, Scipy. The versions I use are listed below:
- Python 3.11
- Numpy 1.26.3
- Pandas 2.2.1
- Scipy 1.12.0
For any question, Please send an email to the author of the paper.