Automated Privacy Policy Annotation with Information Highlighting Made Practical Using Deep Representations

Abdulrahman Alabduljabbar; Ahmed Abusnaina; Ülkü Meteriz-Yildiran; David Mohaisen

doi:10.1145/3460120.3485335

Automated Privacy Policy Annotation with Information Highlighting Made Practical Using Deep Representations

Abdulrahman Alabduljabbar, Ahmed Abusnaina, Ülkü Meteriz-Yildiran, David Mohaisen

University of Central Florida

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

The privacy policy statements are the primary mean for service providers to inform Internet users about their data collection and use practices, although they often are long and lack a specific structure. In this work, we introduce TLDR, a pipeline that employs various deep representation techniques for normalizing policies through learning and modeling, and an automated ensemble classifier for privacy policy classification. TLDR advances the state-of-the-art by (i) categorizing policy contents into nine privacy policy categories with high accuracy, (ii) detecting missing information in privacy policies, and (iii) significantly reducing policy reading time and improving understandability by users.

Original language	English
Title of host publication	CCS 2021 - Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security
Publisher	Association for Computing Machinery
Pages	2378-2380
Number of pages	3
ISBN (Electronic)	9781450384544
DOIs	https://doi.org/10.1145/3460120.3485335
State	Published - 13 Nov 2021
Externally published	Yes
Event	27th ACM Annual Conference on Computer and Communication Security, CCS 2021 - Virtual, Online, Korea, Republic of Duration: 15 Nov 2021 → 19 Nov 2021

Publication series

Name	Proceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)	1543-7221

Conference

Conference	27th ACM Annual Conference on Computer and Communication Security, CCS 2021
Country/Territory	Korea, Republic of
City	Virtual, Online
Period	15/11/21 → 19/11/21

Keywords

annotation automation
deep learning
privacy policy

Access to Document

10.1145/3460120.3485335

Cite this

Alabduljabbar, A., Abusnaina, A., Meteriz-Yildiran, Ü., & Mohaisen, D. (2021). Automated Privacy Policy Annotation with Information Highlighting Made Practical Using Deep Representations. In CCS 2021 - Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (pp. 2378-2380). (Proceedings of the ACM Conference on Computer and Communications Security). Association for Computing Machinery. https://doi.org/10.1145/3460120.3485335

Alabduljabbar, Abdulrahman ; Abusnaina, Ahmed ; Meteriz-Yildiran, Ülkü et al. / Automated Privacy Policy Annotation with Information Highlighting Made Practical Using Deep Representations. CCS 2021 - Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery, 2021. pp. 2378-2380 (Proceedings of the ACM Conference on Computer and Communications Security).

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title = "Automated Privacy Policy Annotation with Information Highlighting Made Practical Using Deep Representations",

abstract = "The privacy policy statements are the primary mean for service providers to inform Internet users about their data collection and use practices, although they often are long and lack a specific structure. In this work, we introduce TLDR, a pipeline that employs various deep representation techniques for normalizing policies through learning and modeling, and an automated ensemble classifier for privacy policy classification. TLDR advances the state-of-the-art by (i) categorizing policy contents into nine privacy policy categories with high accuracy, (ii) detecting missing information in privacy policies, and (iii) significantly reducing policy reading time and improving understandability by users.",

keywords = "annotation automation, deep learning, privacy policy",

author = "Abdulrahman Alabduljabbar and Ahmed Abusnaina and {\"U}lk{\"u} Meteriz-Yildiran and David Mohaisen",

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language = "English",

series = "Proceedings of the ACM Conference on Computer and Communications Security",

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Alabduljabbar, A, Abusnaina, A, Meteriz-Yildiran, Ü & Mohaisen, D 2021, Automated Privacy Policy Annotation with Information Highlighting Made Practical Using Deep Representations. in CCS 2021 - Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. Proceedings of the ACM Conference on Computer and Communications Security, Association for Computing Machinery, pp. 2378-2380, 27th ACM Annual Conference on Computer and Communication Security, CCS 2021, Virtual, Online, Korea, Republic of, 15/11/21. https://doi.org/10.1145/3460120.3485335

Automated Privacy Policy Annotation with Information Highlighting Made Practical Using Deep Representations. / Alabduljabbar, Abdulrahman; Abusnaina, Ahmed; Meteriz-Yildiran, Ülkü et al.
CCS 2021 - Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery, 2021. p. 2378-2380 (Proceedings of the ACM Conference on Computer and Communications Security).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Mohaisen, David

PY - 2021/11/13

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AB - The privacy policy statements are the primary mean for service providers to inform Internet users about their data collection and use practices, although they often are long and lack a specific structure. In this work, we introduce TLDR, a pipeline that employs various deep representation techniques for normalizing policies through learning and modeling, and an automated ensemble classifier for privacy policy classification. TLDR advances the state-of-the-art by (i) categorizing policy contents into nine privacy policy categories with high accuracy, (ii) detecting missing information in privacy policies, and (iii) significantly reducing policy reading time and improving understandability by users.

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KW - deep learning

KW - privacy policy

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M3 - Conference contribution

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Alabduljabbar A, Abusnaina A, Meteriz-Yildiran Ü, Mohaisen D. Automated Privacy Policy Annotation with Information Highlighting Made Practical Using Deep Representations. In CCS 2021 - Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery. 2021. p. 2378-2380. (Proceedings of the ACM Conference on Computer and Communications Security). doi: 10.1145/3460120.3485335

Automated Privacy Policy Annotation with Information Highlighting Made Practical Using Deep Representations

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Keywords

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