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An enhanced disprover algorithm which aims to resolve the limitations in counterexample construction of the latest work and produce higher privacy violations.

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DP-Opt

An enhanced disprover algorithm which aims to resolve the limitations in counterexample construction of the latest work DP-Sniper and produce higher privacy violations.

Install

pip install -r requirements.txt

We used python version 3.8 to run our evaluations.

Basic Usage

To run the evaluation, run the following command:

python dpopt/experiments/__main__.py

The main algorithms DP-Opt and PowerSearcher from the paper can be found in dpopt/search/powersearcher.py and

dpopt/attack/dpopt.py.

Publication

This is the implementation of the algorithms from the following research paper:

Niu B, Zhou Z, Chen Y, et al. DP-Opt: Identify High Differential Privacy Violation by Optimization[C]// L. Wang, M. Segal, J. Chen, et al. Wireless Algorithms, Systems, and Applications, Cham: Springer Nature Switzerland, 2022: 406–416.

Liscence

MIT License, see LICENSE.

This repository is derived from DP-Sniper, marked as MIT License, Copyright (c) 2021 SRI Lab, ETH Zurich.

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An enhanced disprover algorithm which aims to resolve the limitations in counterexample construction of the latest work and produce higher privacy violations.

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