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Self-adaptive privacy concern detection for user-generated content

Vu, Xuan-Son LU and Jiang, Lili (2023) p.153-167
Abstract
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... (More)
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). (Less)
Please use this url to cite or link to this publication:
author
and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
International Conference on Computational Linguistics and Intelligent Text Processing : 19th International Conference, CICLing 2018, Hanoi, Vietnam, March 18–24, 2018, Revised Selected Papers, Part I - 19th International Conference, CICLing 2018, Hanoi, Vietnam, March 18–24, 2018, Revised Selected Papers, Part I
editor
Gelbukh, Alexander
pages
153 - 167
publisher
Springer
external identifiers
  • scopus:85149699287
ISBN
978-3-031-23792-8
978-3-031-23793-5
DOI
10.1007/978-3-031-23793-5_14
language
English
LU publication?
no
id
a82df268-6f18-4e65-8ea1-03eecb99e0c4
date added to LUP
2026-02-11 00:01:33
date last changed
2026-07-28 16:25:12
@inproceedings{a82df268-6f18-4e65-8ea1-03eecb99e0c4,
  abstract     = {{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).}},
  author       = {{Vu, Xuan-Son and Jiang, Lili}},
  booktitle    = {{International Conference on Computational Linguistics and Intelligent Text Processing : 19th International Conference, CICLing 2018, Hanoi, Vietnam, March 18–24, 2018, Revised Selected Papers, Part I}},
  editor       = {{Gelbukh, Alexander}},
  isbn         = {{978-3-031-23792-8}},
  language     = {{eng}},
  month        = {{02}},
  pages        = {{153--167}},
  publisher    = {{Springer}},
  title        = {{Self-adaptive privacy concern detection for user-generated content}},
  url          = {{http://dx.doi.org/10.1007/978-3-031-23793-5_14}},
  doi          = {{10.1007/978-3-031-23793-5_14}},
  year         = {{2023}},
}