research-article

Inferring privacy policies for social networking services

Author:

George DanezisAuthors Info & Claims

AISec '09: Proceedings of the 2nd ACM workshop on Security and artificial intelligence

Pages 5 - 10

https://doi.org/10.1145/1654988.1654991

Published: 09 November 2009 Publication History

Abstract

Social networking sites have come under criticism for their poor privacy protection track record. Yet, there is an inherent difficulty in deciding which principals should have access to user's information or actions, without requiring them to constantly manage their privacy settings. We propose to extract automatically such privacy settings, based on the policy that information produced within a social context should remain in that social context, both to ensure privacy as well as maximising utility. A machine learning approach is used to extract automatically such social contexts, as well as a tentative evaluation.

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Cited By

Singh BCarminati BFerrari E(2021)Privacy-Aware Personal Data Storage (P-PDS): Learning how to Protect User Privacy from External ApplicationsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2019.290380218:2(889-903)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TDSC.2019.2903802
Tonge ACaragea C(2020)Image Privacy Prediction Using Deep Neural NetworksACM Transactions on the Web10.1145/338608214:2(1-32)Online publication date: 9-Apr-2020
https://dl.acm.org/doi/10.1145/3386082
Tonge ACaragea C(2019)Privacy-aware Tag Recommendation for Accurate Image Privacy PredictionACM Transactions on Intelligent Systems and Technology10.1145/333505410:4(1-28)Online publication date: 12-Aug-2019
https://dl.acm.org/doi/10.1145/3335054
Show More Cited By

Index Terms

Inferring privacy policies for social networking services
1. Security and privacy
  1. Human and societal aspects of security and privacy
2. Social and professional topics
  1. Computing / technology policy
    1. Privacy policies

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Published In

cover image ACM Conferences

AISec '09: Proceedings of the 2nd ACM workshop on Security and artificial intelligence

November 2009

72 pages

ISBN:9781605587813

DOI:10.1145/1654988

Program Chairs:
Dirk Balfanz
Google, USA
,
Jessica Staddon
PARC, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 November 2009

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CCS '09

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SIGSAC

CCS '09: 16th ACM Conference on Computer and Communications Security 2009

November 9, 2009

Illinois, Chicago, USA

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Overall Acceptance Rate 94 of 231 submissions, 41%

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Cited By

Singh BCarminati BFerrari E(2021)Privacy-Aware Personal Data Storage (P-PDS): Learning how to Protect User Privacy from External ApplicationsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2019.290380218:2(889-903)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TDSC.2019.2903802
Tonge ACaragea C(2020)Image Privacy Prediction Using Deep Neural NetworksACM Transactions on the Web10.1145/338608214:2(1-32)Online publication date: 9-Apr-2020
https://dl.acm.org/doi/10.1145/3386082
Tonge ACaragea C(2019)Privacy-aware Tag Recommendation for Accurate Image Privacy PredictionACM Transactions on Intelligent Systems and Technology10.1145/333505410:4(1-28)Online publication date: 12-Aug-2019
https://dl.acm.org/doi/10.1145/3335054
Khazaei TXiao LMercer RKhan ALee DSastry NWeber I(2018)Understanding Privacy Dichotomy in TwitterProceedings of the 29th on Hypertext and Social Media10.1145/3209542.3209564(156-164)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209542.3209564
Yu JKuang ZZhang BZhang WLin DFan J(2018)Leveraging Content Sensitiveness and User Trustworthiness to Recommend Fine-Grained Privacy Settings for Social Image SharingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2017.278798613:5(1317-1332)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1109/TIFS.2017.2787986
Akcora CFerrari E(2018)Graphical User Interfaces for Privacy SettingsEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_360(966-978)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_360
Misra GSuch J(2017)REACTProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201710.1145/3110025.3110073(421-426)Online publication date: 31-Jul-2017
https://dl.acm.org/doi/10.1145/3110025.3110073
Lee HKobsa A(2017)Privacy preference modeling and prediction in a simulated campuswide IoT environment2017 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM.2017.7917874(276-285)Online publication date: Mar-2017
https://doi.org/10.1109/PERCOM.2017.7917874
Yu JKuang ZYu ZLin DFan J(2017)Privacy Setting Recommendation for Image Sharing2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2017.00-73(726-730)Online publication date: Dec-2017
https://doi.org/10.1109/ICMLA.2017.00-73
Singh BCarminati BFerrari E(2017)Learning Privacy Habits of PDS Owners2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS.2017.65(151-161)Online publication date: Jun-2017
https://doi.org/10.1109/ICDCS.2017.65
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