PAS-MC: Privacy-preserving Analytics Stream for the Mobile Cloud

TL;DR

PAS-MC is a practical system enabling privacy-preserving, anonymous stream analytics on personal data, balancing data privacy, accuracy, and resistance to traffic analysis attacks in mobile cloud environments.

Contribution

It introduces PAS-MC, the first system to provide practical privacy-preserving and anonymity stream analytics for mobile cloud data, with local data privatization and minimal trust vulnerabilities.

Findings

Successfully streams vehicular location data with high accuracy.

Ensures privacy and anonymity against traffic analysis attacks.

Demonstrates effectiveness on California Transportation Dataset.

Abstract

In today's digital world, personal data is being continuously collected and analyzed without data owners' consent and choice. As data owners constantly generate data on their personal devices, the tension of storing private data on their own devices yet allowing third party analysts to perform aggregate analytics yields an interesting dilemma. This paper introduces PAS-MC, the first practical privacy-preserving and anonymity stream analytics system. PAS-MC ensures that each data owner locally privatizes their sensitive data before responding to analysts' queries. PAS-MC also protects against traffic analysis attacks with minimal trust vulnerabilities.We evaluate the scheme over the California Transportation Dataset and show that we can privately and anonymously stream vehicular location updates yet preserve high accuracy.