000			01428nam a2200169 a 4500
005			20260901030333.0
008			250101s2020 xx o 000 0 eng d
100	1		_aMengmeng Yang
245	1	0	_aSecure Hot Path Crowdsourcing with Local Differential Privacy under Fog Computing Architecture
264		1	_barXiv _c2020
336			_atext
338			_aonline resource
520			_aCrowdsourcing 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
506	0		_aOpen access — freely available to read.
856	4	0	_uhttps://arxiv.org/pdf/2012.13807v1 _yRead the full paper (PDF)
942			_cERES
999			_c648 _d648