01428nam a2200169 a 4500 20260901030333.0 250101s2020 xx o 000 0 eng d Mengmeng Yang Secure Hot Path Crowdsourcing with Local Differential Privacy under Fog Computing Architecture arXiv 2020 text online resource 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 metho Open access — freely available to read. https://arxiv.org/pdf/2012.13807v1 Read the full paper (PDF) ERES 648 648 0 0 0 3 CYB MAIN MAIN SCICOMP 2026-09-01 0 YGE000909 2026-09-01 03:03:33 https://arxiv.org/pdf/2012.13807v1 2026-09-01 PAPER