  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.
