Secure Hot Path Crowdsourcing With Local Differential Privacy Under Fog Computing Architecture

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