Self-adaptive Privacy Concern Detection for User-generated Content

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 chall...

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Bibliographic Details
Published in:arXiv.org 2018-06
Main Authors: Xuan-Son Vu, Jiang, Lili
Format: Article
Language:English
Subjects:
Cold starts
Data analysis
Masks
Noise
Noise sensitivity
Privacy
User generated content
Online Access:Get full text
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Summary: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).
ISSN:2331-8422