Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147885
Title: Secure hot path crowdsourcing with local differential privacy under fog computing architecture
Authors: Yang, Mengmeng
Tjuawinata, Ivan
Lam, Kwok-Yan
Zhao, Jun
Sun, Lin
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Yang, M., Tjuawinata, I., Lam, K., Zhao, J. & Sun, L. (2020). Secure hot path crowdsourcing with local differential privacy under fog computing architecture. IEEE Transactions On Services Computing. https://dx.doi.org/10.1109/TSC.2020.3039336
Journal: IEEE Transactions on Services Computing 
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. 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 statistic, 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.
URI: https://hdl.handle.net/10356/147885
ISSN: 1939-1374
DOI: 10.1109/TSC.2020.3039336
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TSC.2020.3039336.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:NTC Journal Articles

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