Privacy-preserving smart nudging system: resistant to traffic analysis and data breach

Abstract

A solution like Green Transportation Choices with IoT and Smart Nudging (SN) is aiming to resolve urban challenges (e.g., increased traffic, congestion, air pollution, and noise pollution) by influencing people towards environment-friendly decisions in their daily life. The essential aspect of this system is to construct personalized suggestion and positive reinforcement for people to achieve environmentally preferable outcomes. However, the process of tailoring a nudge for a specific person requires a significant amount of personal data (e.g., user's location data, health data, activity and more) analysis.

People are willingly giving up their private data for the greater good of society and making SN system a target for adversaries to get people's data and misuse them. Yet, preserving user privacy is subtly discussed and often overlooked in the SN system. Meanwhile, the European union's General data protection regulation (GDPR) tightens European Unions's (EU) already stricter privacy policy. Thus, preserving user privacy is inevitable for a system like SN.

Privacy-preserving smart nudging (PPSN) is a new middleware that gives privacy guarantee for both the users and the SN system and additionally offers GDPR compliance. In the PPSN system, users have the full autonomy of their data, and users data is well protected and inaccessible without the participation of the data owner. In addition to that, PPSN system gives protection against adversaries that control all the server but one, observe network traffics and control malicious users. PPSN system's primary insight is to encrypt as much as observable variables if not all and hide the remainder by adding noise. A prototype implementation of the PPSN system achieves a throughput of 105 messages per second with 24 seconds end-to-end latency for 125k users on a quadcore machine and scales linearly with the number of users.