Toward automated multiparty privacy conflict detection

Haoti Zhong; Anna Squicciarini; David Miller

doi:10.1145/3269206.3269329

Toward automated multiparty privacy conflict detection

Haoti Zhong, Anna Squicciarini, David Miller

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

12 Scopus citations

Abstract

In an effort to support users' decision making process in regards to shared and co-managed online images, in this paper we present a novel model to early detect images which may be subject to possible conflicting access control decisions. We present a group-based stochastic model able to identify potential privacy conflicts among multiple stakeholders of an image. We discuss experiments on a dataset of over 3000 online images. Our approach outperforms all baselines, even the strong ones based on a Convolutional Neural Network architecture.

Original language	English (US)
Title of host publication	CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
Editors	Norman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
Publisher	Association for Computing Machinery
Pages	1811-1814
Number of pages	4
ISBN (Electronic)	9781450360142
DOIs	https://doi.org/10.1145/3269206.3269329
State	Published - Oct 17 2018
Event	27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy Duration: Oct 22 2018 → Oct 26 2018

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings

Other

Other	27th ACM International Conference on Information and Knowledge Management, CIKM 2018
Country/Territory	Italy
City	Torino
Period	10/22/18 → 10/26/18

All Science Journal Classification (ASJC) codes

General Business, Management and Accounting
General Decision Sciences

Access to Document

10.1145/3269206.3269329

Cite this

Zhong, H., Squicciarini, A., & Miller, D. (2018). Toward automated multiparty privacy conflict detection. In N. Paton, S. Candan, H. Wang, J. Allan, R. Agrawal, A. Labrinidis, A. Cuzzocrea, M. Zaki, D. Srivastava, A. Broder, & A. Schuster (Eds.), CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 1811-1814). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3269206.3269329

Zhong, Haoti ; Squicciarini, Anna ; Miller, David. / Toward automated multiparty privacy conflict detection. CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management. editor / Norman Paton ; Selcuk Candan ; Haixun Wang ; James Allan ; Rakesh Agrawal ; Alexandros Labrinidis ; Alfredo Cuzzocrea ; Mohammed Zaki ; Divesh Srivastava ; Andrei Broder ; Assaf Schuster. Association for Computing Machinery, 2018. pp. 1811-1814 (International Conference on Information and Knowledge Management, Proceedings).

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abstract = "In an effort to support users' decision making process in regards to shared and co-managed online images, in this paper we present a novel model to early detect images which may be subject to possible conflicting access control decisions. We present a group-based stochastic model able to identify potential privacy conflicts among multiple stakeholders of an image. We discuss experiments on a dataset of over 3000 online images. Our approach outperforms all baselines, even the strong ones based on a Convolutional Neural Network architecture.",

author = "Haoti Zhong and Anna Squicciarini and David Miller",

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Zhong, H, Squicciarini, A & Miller, D 2018, Toward automated multiparty privacy conflict detection. in N Paton, S Candan, H Wang, J Allan, R Agrawal, A Labrinidis, A Cuzzocrea, M Zaki, D Srivastava, A Broder & A Schuster (eds), CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, pp. 1811-1814, 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 10/22/18. https://doi.org/10.1145/3269206.3269329

Toward automated multiparty privacy conflict detection. / Zhong, Haoti; Squicciarini, Anna ; Miller, David.
CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ed. / Norman Paton; Selcuk Candan; Haixun Wang; James Allan; Rakesh Agrawal; Alexandros Labrinidis; Alfredo Cuzzocrea; Mohammed Zaki; Divesh Srivastava; Andrei Broder; Assaf Schuster. Association for Computing Machinery, 2018. p. 1811-1814 (International Conference on Information and Knowledge Management, Proceedings).

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

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BT - CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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Zhong H, Squicciarini A , Miller D. Toward automated multiparty privacy conflict detection. In Paton N, Candan S, Wang H, Allan J, Agrawal R, Labrinidis A, Cuzzocrea A, Zaki M, Srivastava D, Broder A, Schuster A, editors, CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery. 2018. p. 1811-1814. (International Conference on Information and Knowledge Management, Proceedings). doi: 10.1145/3269206.3269329

Toward automated multiparty privacy conflict detection

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