Optimal noise functions for location privacy on continuous regions

Optimal data-independent noise for differential privacy

ε-Differential privacy is a property that seeks to characterize privacy in data sets. It is formulated as a query-response method, and computationally achieved by output perturbation. Several noise-addition methods to implement such output perturbation have been proposed in the literature. We focus on data-independent noise, that is, noise whose distribution is constant across data sets. Our go...

Optimal Trajectory Partitioning for Enhanced Privacy and Utility in Continuous Location Based Services

Enhancing Customer Privacy for Commercial Continuous Location-Based Services

The likelihood of consumers to use commercial location-based services significantly depends on their perception of privacy protection by the service provider. In this paper we discuss existing privacy-enhancing architectures for LBS and argue that they are either not applicable or insufficient for services requiring continuous location queries. In order to offer such services providers often pr...

Privacy-Preserving Aggregate Queries for Optimal Location Selection

Today, vast amounts of location data are collected by various service providers. The location data owners have a good idea of where their customers are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot directly share their data with other businesses, mainly due ...

Towards optimal noise distribution for privacy preserving in data aggregation

In aggregation applications, individual privacy is a crucial factor to determine the effectiveness, for which the noise-addition method (i.e., a random noise value is added to the true value) is a simple yet powerful approach. However, improper additive noise could result in bias for the aggregate result. It demands an optimal noise distribution to reduce the deviation. In this paper, we develo...