DPMon: a Differentially-Private Query Engine for Passive Measurements

TL;DR

DPMon is a privacy-preserving query engine for passive network measurements that uses differential privacy to enable meaningful data analysis while protecting user privacy in large-scale data environments.

Contribution

It introduces DPMon, a novel tool leveraging differential privacy and big data infrastructure to securely analyze passive network traffic data.

Findings

DPMon effectively balances data utility and privacy.

It operates efficiently on Apache Spark.

It enables meaningful insights without compromising privacy.

Abstract

Passive monitoring is a network measurement technique which analyzes the traffic carried by an operational network. It has several applications for traffic engineering, Quality of Experience monitoring and cyber security. However, it entails the processing of personal information, thus, threatening users' privacy. In this work, we propose DPMon, a tool to run privacy-preserving queries to a dataset of passive network measurements. It exploits differential privacy to perturb the output of the query to preserve users' privacy. DPMon can exploit big data infrastructures running Apache Spark and operate on different data formats. We show that DPMon allows extracting meaningful insights from the data, while at the same time controlling the amount of disclosed information.