GeoPrivacy 0.0.2

A python package for implementing geo-indistinguishability privacy mechanisms

GeoPrivacy

This is a python package for implementing geo-indistinguishability privacy mechanism. The methods can be applied to various location privacy tasks. So far, we implemented two privacy mechanisms for the sporadic case as described in the paper Geo-indistinguishability: A Principled Approach to Location Privacy. In the exemple notebook, we demonstrated the mechanisms using NYC taxi trip data for sporadic case.

Installation

Please run the following command in terminal to install the package and other dependencies.

$ pip install GeoPrivacy==0.0.1

pip install GeoPrivacy==0.0.1

Example Usage

To generate a random laplace noise and add it to a location:

from GeoPrivacy.mechanism import random_laplace_noise
eps = 0.1
location = (3, 5)
noise = random_laplace_noise(eps)
new_location = location + noise

Given a list of representative locations and their corresponding weights, you can use the geo-indistinguishable mechanism of optimal utility as following:

from GeoPrivacy import mechanism, metrics
x_list = [(1, 2), (3, 2), (4, 1)]
pi_list = [3, 2, 5]
dQ = metrics.euclidean
epsilon = 0.5

p_matrix, pre_prob, post_prob = optql(x_list, pi_list, dQ, epsilon=0.5)

For more examples, please see example.ipynb. Other information can be found in the docstring.

Theory

The planar laplace mechanism can be used to add laplace noise to any given 2d-coordinates. It's worth noting that polar coordinates are used to generate random laplace noise. In order to randomly draw r, we use the inverse CDF of r which is as calculated in https://hal.inria.fr/hal-01114241/file/paper.pdf.

The optimal utility mechanism divides the region of interest into subregions and builds a linear program using representative points chosen from each subregion. Then, by solving the linear program, a stochastic transition matrix is obtained which can be used to transform any other data from the same region. The linear program takes the following form: