Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach

Yizhi Liu, Fang Yu Lin, Mohammadreza Ebrahimi, Weifeng Li, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Internet users have been exposing an increasing amount of Personally Identifiable Information (PII) on social media. Such exposed PII can be exploited by cybercriminals and cause severe losses to the users. Informing users of their PII exposure in social media is crucial to raise their privacy awareness and encourage them to take protective measures. To this end, advanced techniques are needed to extract users' exposed PII in social media automatically, whereas most existing studies remain manual. While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency. However, DL-based IE models often require large-scale labeled data for training, but PII-labeled social media posts are difficult to obtain due to privacy concerns. Also, these models rely heavily on pre-trained word embeddings, while PII in social media often varies in forms and thus has no fixed representations in pre-trained word embeddings. In this study, we propose the Deep Transfer Learning for PII Extraction (DTL-PIIE) framework to address these two limitations. DTL-PIIE transfers knowledge learned from publicly available PII data to social media in order to address the problem of rare PII-labeled data. Moreover, our framework leverages Graph Convolutional Networks (GCNs) to incorporate syntactic patterns to guide PIIE without relying on pre-trained word embeddings. Evaluation against benchmark IE models indicates that our approach outperforms state-of-the-art DL-based IE models. An ablation analysis further confirms the efficacy of each component in our model. Our proposed framework can facilitate various applications, such as PII misuse prediction and privacy risk assessment, thereby protecting the privacy of internet users.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Intelligence and Security Informatics, ISI 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665438384
DOIs
StatePublished - 2021
Externally publishedYes
Event19th Annual IEEE International Conference on Intelligence and Security Informatics, ISI 2021 - Virtual, Online, United States
Duration: Nov 2 2021Nov 3 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Intelligence and Security Informatics, ISI 2021

Conference

Conference19th Annual IEEE International Conference on Intelligence and Security Informatics, ISI 2021
Country/TerritoryUnited States
CityVirtual, Online
Period11/2/2111/3/21

Keywords

  • PII
  • deep transfer learning
  • information extraction
  • privacy
  • social media

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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