Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

TL;DR

This paper comprehensively analyzes how differential privacy techniques can be applied to Internet-of-Vehicles systems, highlighting tradeoffs, challenges, and opportunities for privacy-preserving data sharing and processing in vehicular networks.

Contribution

It provides the first detailed characterization of differential privacy mechanisms tailored for IoV, including an in-depth study of deep neural network applications within this context.

Findings

Differential privacy introduces significant tradeoffs between privacy and service quality.

Certain DP mechanisms can be effectively integrated with in-vehicle deep learning applications.

There are numerous challenges and opportunities for optimizing privacy-preserving IoV systems.

Abstract

Recent developments of advanced Human-Vehicle Interactions rely on the concept Internet-of-Vehicles (IoV), to achieve large-scale communications and synchronizations of data in practice. The concept of IoV is highly similar to a distributed system, where each vehicle is considered as a node and all nodes are grouped with a centralized server. In this manner, the concerns of data privacy are significant since all vehicles collect, process and share personal statistics (e.g. multi-modal, driving statuses and etc.). Therefore, it's important to understand how modern privacy-preserving techniques suit for IoV. We present the most comprehensive study to characterize modern privacy-preserving techniques for IoV to date. We focus on Differential Privacy (DP), a representative set of mathematically-guaranteed mechanisms for both privacy-preserving processing and sharing on sensitive data. The purpose of our study is to demystify the tradeoffs of deploying DP techniques, in terms of service quality. We first characterize representative privacy-preserving processing mechanisms, enabled by advanced DP approaches. Then we perform a detailed study of an emerging in-vehicle, Deep-Neural-Network-driven application, and study the upsides and downsides of DP for diverse types of data streams. Our study obtains 11 key findings and we highlight FIVE most significant observations from our detailed characterizations. We conclude that there are a large volume of challenges and opportunities for future studies, by enabling privacy-preserving IoV with low overheads for service quality.