LDP-hierarchical-mechanism 0.1.1

Local Differential Privacy for Range Queries

Hierarchical Mechanism for LDP

it is an implementation of the Hierarchical Mechanism in

Kulkarni, Tejas, Graham Cormode, and Divesh Srivastava. "Answering range queries under local differential privacy." arXiv preprint arXiv:1812.10942 (2018).

The LDP frequency protocol are implemented from the library https://github.com/Samuel-Maddock/pure-LDP

The library is intended for testing the LDP hierarchical mechanism locally.

Install

pip install hierarchical-mechanism-LDP

Usage

It is based on the class Private_Tree that implements the hierarchical mechanism for local differential privacy. The class has the following methods:

Initialization

# Initialization

from hierarchical_mechanism_LDP import Private_TreeBary
import numpy as np

B = 4000  # bound of the data, i.e., the data is in [0, B]
b = 4  # branching factor of the tree
eps = 1  # privacy budget
q = 0.4  # quantile to estimate
protocol = 'unary_encoding'  # protocol to use for LDP frequency estimation

tree = Private_TreeBary(B, b)

data = np.random.randint(0, B, 100000)  # generate random data

tree.update_tree(data, eps, protocol, post_process=True)  # update the tree with the data

The post_process boolean parameter is used to apply the post-processing step of the hierarchical mechanism. The post-processing step is used to improve the accuracy of the mechanism. The post-processing step is applied by default.

Post-processing applies the Hierarchial Mechanism proposed by Hay et al. in

M. Hay, V. Rastogi, G. Miklau, and D. Suciu. Boosting the accuracy of differentially private histograms through consistency. PVLDB, 3(1):1021–1032, 2010.

Quantile estimation

You can estimate the quantile of the data with Private_Tree.get_quantile(q), where q is the quantile to estimate.

# get quantile of the data
true_quantile = np.quantile(data, q)
private_quantile = tree.get_quantile(q)  # get the quantile
print(f"DP quantile: {private_quantile}")
print(f"True quantile: {true_quantile}")

Result

Private quantile: 1591
True quantile: 1598.0

Range Queries

You can estimate the range queries of the data with Private_Tree.get_range_query(a, b), where a and b are the bounds of the range query. Additionally, you can return a normalized range query.

left = 1000
right = 2000
true_range_query = np.sum(data >= left) - np.sum(data >= right)
private_range_query = tree.get_range_query(left, right, normalized=False)
print(f"True range query: {true_range_query}")
print(f"Private range query: {private_range_query}")

Result

True range query: 24980
Private range query: 25514.970123615636

Binning

Given a list of quantiles and an error alpha, you can create bins of the form [q_i - alpha, q_i + alpha] with Private_Tree.get_bins(quantiles, alpha). The bins are returned as a list of tuples.

# test binning
quantiles = [0.25, 0.50, 0.75]
alpha = 0.1
bins = tree.get_bins(quantiles, alpha)
print(bins)

Result

[(np.int64(664), np.int64(1441)), (np.int64(1591), np.int64(2562)), (np.int64(2669), np.int64(3365))]