Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147885
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dc.contributor.authorYang, Mengmengen_US
dc.contributor.authorTjuawinata, Ivanen_US
dc.contributor.authorLam, Kwok-Yanen_US
dc.contributor.authorZhao, Junen_US
dc.contributor.authorSun, Linen_US
dc.date.accessioned2021-12-08T13:58:47Z-
dc.date.available2021-12-08T13:58:47Z-
dc.date.issued2020-
dc.identifier.citationYang, 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.2020.3039336en_US
dc.identifier.issn1939-1374en_US
dc.identifier.urihttps://hdl.handle.net/10356/147885-
dc.description.abstractCrowdsourcing 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.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Services Computingen_US
dc.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.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleSecure hot path crowdsourcing with local differential privacy under fog computing architectureen_US
dc.typeJournal Articleen
dc.contributor.researchResearch Techno Plazaen_US
dc.contributor.researchStrategic Centre for Research in Privacy-Preserving Technologies & Systemsen_US
dc.identifier.doi10.1109/TSC.2020.3039336-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85096853287-
dc.subject.keywordsAdditive Secret Sharingen_US
dc.subject.keywordsLocal Differential Privacyen_US
dc.description.acknowledgementThis research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Strategic Capability Research Centres Funding Initiative.en_US
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