Local Obfuscation Mechanisms for Hiding Probability Distributions

12/03/2018
by   Yusuke Kawamoto, et al.
0

We introduce a formal model for the information leakage of probability distributions and define a notion called distribution privacy. Roughly, the distribution privacy of a local obfuscation mechanism means that the attacker cannot significantly gain any information on the distribution of the mechanism's input by observing its output. Then we show that existing local mechanisms can hide input distributions in terms of distribution privacy, while deteriorating the utility by adding too much noise. For example, we prove that the Laplace mechanism needs to add a large amount of noise proportionally to the infinite Wasserstein distance between the two distributions we want to make indistinguishable. To improve the tradeoff between distribution privacy and utility, we introduce a local obfuscation mechanism, called a tupling mechanism, that adds random dummy data to the output. Then we apply this mechanism to the protection of user attributes in location based services. By experiments, we demonstrate that the tupling mechanism outperforms popular local mechanisms in terms of attribute obfuscation and service quality.

READ FULL TEXT
research
12/03/2018

Differentially Private Obfuscation Mechanisms for Hiding Probability Distributions

We propose a formal model for the privacy of user attributes in terms of...
research
04/01/2019

Generating Optimal Privacy-Protection Mechanisms via Machine Learning

We consider the problem of obfuscating sensitive information while prese...
research
05/24/2018

Optimal noise functions for location privacy on continuous regions

Users of location-based services (LBSs) are highly vulnerable to privacy...
research
07/13/2019

Local Distribution Obfuscation via Probability Coupling

We introduce a general model for the local obfuscation of probability di...
research
11/14/2018

On the Robustness of Information-Theoretic Privacy Measures and Mechanisms

Consider a data publishing setting for a dataset composed of non-private...
research
02/15/2021

Genomic Data Sharing under Dependent Local Differential Privacy

Privacy-preserving genomic data sharing is prominent to increase the pac...
research
09/12/2023

Systematic Evaluation of Geolocation Privacy Mechanisms

Location data privacy has become a serious concern for users as Location...

Please sign up or login with your details

Forgot password? Click here to reset