Secure Hot Path Crowdsourcing with Local Differential Privacy under Fog Computing Architecture

TL;DR

This paper introduces a trie-based iterative statistic method combining secret sharing and local differential privacy to securely and accurately report hot travel paths in IoT crowdsourcing, even with limited participants.

Contribution

It proposes a novel privacy-preserving method for crowdsourcing hot path data that is efficient for resource-limited devices and effective with few participants.

Findings

Provides strict privacy guarantees

Significantly improves accuracy over existing solutions

Operates efficiently on resource-constrained devices

Abstract

Crowdsourcing plays an essential role in the Internet of Things (IoT) for data collection, where a group of workers is equipped with Internet-connected geolocated devices to collect sensor data for marketing or research purpose. In this paper, we consider crowdsourcing these worker's hot travel path. Each worker is required to report his real-time location information, which is sensitive and has to be protected. Encryption-based methods are the most direct way to protect the location, but not suitable for resource-limited devices. Besides, local differential privacy is a strong privacy concept and has been deployed in many software systems. However, the local differential privacy technology needs a large number of participants to ensure the accuracy of the estimation, which is not always the case for crowdsourcing. To solve this problem, we proposed a trie-based iterative statistic method, which combines additive secret sharing and local differential privacy technologies. The proposed method has excellent performance even with a limited number of participants without the need of complex computation. Specifically, the proposed method contains three main components: iterative statistics, adaptive sampling, and secure reporting. We theoretically analyze the effectiveness of the proposed method and perform extensive experiments to show that the proposed method not only provides a strict privacy guarantee, but also significantly improves the performance from the previous existing solutions.