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Self-adaptive Privacy Concern Detection for User-generated Content
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Database and Data Mining Group)ORCID iD: 0000-0001-8820-2405
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Database and Data Mining Group)
2018 (English)In: 19th International Conference on Computational Linguistics and Intelligent Text Processing, Hanoi, Vietnam, 2018Conference paper, Published paper (Refereed)
Abstract [en]

To protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual’s sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some mechanisms to protect data privacy in different scenarios, most of the existing studies assume uniform privacy concerns for all individuals. Consequently, putting an equal amount of noise to all individuals leads to insufficient privacy protection for some users, while over-protecting others. To address this issue, we propose a self-adaptive approach for privacy concern detection based on user personality. Our experimental studies demonstrate the effectiveness to address a suitable personalized privacy protection for cold-start users (i.e., without their privacy-concern information in training data).

Place, publisher, year, edition, pages
2018.
Series
Lecture Notes in Computer Science (LNCS),
Keywords [en]
privacy-guaranteed data analysis, deep learning, multi-layer perceptron
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:umu:diva-146470OAI: oai:DiVA.org:umu-146470DiVA, id: diva2:1196463
Conference
19th International Conference on Computational Linguistics and Intelligent Text Processing
Projects
Privacy-aware Data FederationAvailable from: 2018-04-10 Created: 2018-04-10 Last updated: 2018-06-09

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Vu, Xuan-SonLili, Jiang

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
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  • asciidoc
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