Sourse code for the paper Optimizing Fitness-For-Use of Differentially Private Linear Queries.
Here is an extensible julia code base for solving the optimization problems.
This is a convex optimization problem with inequality constraints. We use the barrier method to solve this problem. For the newton's step, conjugate gradient method is used to avoid calculation of inverse matrix. Then we use Armijo's rule to determine the step size.
The algorithms are implemented in the following files.
- softmax.py: Implementation of the algorithms SM-II, IP, HM
- convexdp.py: Implementation of algorithms CA, wCA-I, wCA-II
- The pseudo-code of CA algorithm can be found in the appendix of the paper Convex Optimization for Linear Query Processing under Approximate Differential Privacy.
The following code solves the Fitness-for-use problem using SM-II algorithm, you need to specify basis as the basis matrix, index as the index matrix and bound as the accuracy constraints.
from softmax import configuration, matrix_query
args = configuration()
# basis matrix is B
# index matrix is L
# bound vector is c
mat_opt = matrix_query(args, basis, index, bound)
mat_opt.optimize()
# scale the covariacen matrix
# make it satisfy all accuracy constraints
mat_cov = mat_opt.cov/np.max(mat_opt.f_var)The following files have the experiments in the paper. Note that due to randomness and different machine performance, the result may not be exactly the same as the numbers in the paper. It's suggested that you start from small workloads (set the number of queries param_m = 64). For large workloads (param_m = 1024), it could take hours to finish, you can speed up by setting args.maxitercg = 3, or try to use cupy instead of numpy.
- 1_range.py: Experiment for range queries (section 7.4)
- 2_discrete.py: Experiment for random queries (section 7.5)
- 3_pl94.py: Experiment for PL-94 (section 7.3)
- It takes around 10k iterations and 430 seconds.
- 4_age.py: Experiment for Age Pyramids (section 7.6)
- it takes around 5k iterations and 160 seconds.
- 5_marginal.py: Experiment for marginals (section 7.7)
- For t=16, it takes around 3 days to finish.
- 6_related.py: Experiment for related queries (section7.8)
Simply run the main function, change the parameters for different settings. You can also change the hyperparameters in the code to achieve more accurate result, check softmax.configuration() for details of tunable hyperparameters.
You are encouraged to cite the paper Optimizing Fitness-For-Use of Differentially Private Linear Queries if you use this tool for academic research:
@article{xiao2020optimizing,
title={Optimizing Fitness-For-Use of Differentially Private Linear Queries},
author={Xiao, Yingtai and Ding, Zeyu and Wang, Yuxin and Zhang, Danfeng and Kifer, Daniel},
journal={arXiv preprint arXiv:2012.00135},
year={2020}
}MIT.