research-article

Fine-Grained Privacy Detection with Graph-Regularized Hierarchical Attentive Representation Learning

Authors:

Liqiang NieAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 38, Issue 4

Article No.: 37, Pages 1 - 26

https://doi.org/10.1145/3406109

Published: 16 September 2020 Publication History

Abstract

Due to the complex and dynamic environment of social media, user generated contents (UGCs) may inadvertently leak users’ personal aspects, such as the personal attributes, relationships and even the health condition, and thus place users at high privacy risks. Limited research efforts, thus far, have been dedicated to the privacy detection from users’ unstructured data (i.e., UGCs). Moreover, existing efforts mainly focus on applying conventional machine learning techniques directly to traditional hand-crafted privacy-oriented features, ignoring the powerful representing capability of the advanced neural networks. In light of this, in this article, we present a fine-grained privacy detection network (GrHA) equipped with graph-regularized hierarchical attentive representation learning. In particular, the proposed GrHA explores the semantic correlations among personal aspects with graph convolutional networks to enhance the regularization for the UGC representation learning, and, hence, fulfil effective fine-grained privacy detection. Extensive experiments on a real-world dataset demonstrate the superiority of the proposed model over state-of-the-art competitors in terms of eight standard metrics. As a byproduct, we have released the codes and involved parameters to facilitate the research community.

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Cited By

Liang ZGuo JQiu WHuang ZLi S(2024)When graph convolution meets double attention: online privacy disclosure detection with multi-label text classificationData Mining and Knowledge Discovery10.1007/s10618-023-00992-y38:3(1171-1192)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10618-023-00992-y
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Müllner PLex ESchedl MKowald D(2023)ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/3608481Online publication date: 13-Jul-2023
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Index Terms

Fine-Grained Privacy Detection with Graph-Regularized Hierarchical Attentive Representation Learning
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 38, Issue 4

October 2020

375 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3402434

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 16 September 2020

Accepted: 01 June 2020

Revised: 01 May 2020

Received: 01 November 2019

Published in TOIS Volume 38, Issue 4

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National Key Research and Development Project of New Generation Artificial Intelligence
Innovation Teams in Colleges and Universities in Jinan
National Natural Science Foundation of China
Shandong Provincial Key Research and Development Program
Shandong Provincial Natural Science Foundation

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https://dl.acm.org/doi/10.1007/s10618-023-00992-y
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Müllner PLex ESchedl MKowald D(2023)ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/3608481Online publication date: 13-Jul-2023
https://doi.org/10.1145/3608481
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Yan SLin KZheng XWang H(2022)LkeRec: Toward Lightweight End-to-End Joint Representation Learning for Building Accurate and Effective RecommendationACM Transactions on Information Systems10.1145/348667340:3(1-28)Online publication date: 31-Jul-2022
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Guan WWen HSong XWang CYeh CChang XNie L(2022)Partially Supervised Compatibility ModelingIEEE Transactions on Image Processing10.1109/TIP.2022.318729031(4733-4745)Online publication date: 2022
https://doi.org/10.1109/TIP.2022.3187290
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