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Personalized privacy protection in social networks

Editors: José Blakeley, Joseph M. Hellerstein, Nick Koudas, Wolfgang Lehner, Sunita Sarawagi, Uwe Röhm Authors:

Philip S. YuAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 4, Issue 2

Pages 141 - 150

https://doi.org/10.14778/1921071.1921080

Published: 01 November 2010 Publication History

Abstract

Due to the popularity of social networks, many proposals have been proposed to protect the privacy of the networks. All these works assume that the attacks use the same background knowledge. However, in practice, different users have different privacy protect requirements. Thus, assuming the attacks with the same background knowledge does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advantage of differences of users' privacy requirements. In this paper, we introduce a framework which provides privacy preserving services based on the user's personal privacy requests. Specifically, we define three levels of protection requirements based on the gradually increasing attacker's background knowledge and combine the label generalization protection and the structure protection techniques (i.e. adding noise edge or nodes) together to satisfy different users' protection requirements. We verify the effectiveness of the framework through extensive experiments.

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Cited By

Shetty NMuniyal BDokania ADatta SGandluri MMaben LPriyanshu A(2023)Guarding Your Social CircleSecurity and Communication Networks10.1155/2023/25489622023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/2548962
Li XChen LWu D(2023)Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute PrivacyACM Transactions on Knowledge Discovery from Data10.1145/361409818:2(1-24)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3614098
Wei YFu XYan DSun QPeng HWu JWang JLi X(2023)Heterogeneous graph neural network with semantic-aware differential privacy guaranteesKnowledge and Information Systems10.1007/s10115-023-01895-665:10(4085-4110)Online publication date: 15-May-2023
https://dl.acm.org/doi/10.1007/s10115-023-01895-6
Show More Cited By

Index Terms

Personalized privacy protection in social networks
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
2. Information systems
  1. Data management systems
    1. Database management system engines
  2. Information systems applications

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 4, Issue 2

November 2010

105 pages

ISSN:2150-8097

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VLDB Endowment

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Published: 01 November 2010

Published in PVLDB Volume 4, Issue 2

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Cited By

Shetty NMuniyal BDokania ADatta SGandluri MMaben LPriyanshu A(2023)Guarding Your Social CircleSecurity and Communication Networks10.1155/2023/25489622023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/2548962
Li XChen LWu D(2023)Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute PrivacyACM Transactions on Knowledge Discovery from Data10.1145/361409818:2(1-24)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3614098
Wei YFu XYan DSun QPeng HWu JWang JLi X(2023)Heterogeneous graph neural network with semantic-aware differential privacy guaranteesKnowledge and Information Systems10.1007/s10115-023-01895-665:10(4085-4110)Online publication date: 15-May-2023
https://dl.acm.org/doi/10.1007/s10115-023-01895-6
Miao XZhang WJiang YFu FShao YChen LTao YCao GCui B(2022)PCG: a privacy preserving collaborative graph neural network training frameworkThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00768-832:4(717-736)Online publication date: 4-Nov-2022
https://dl.acm.org/doi/10.1007/s00778-022-00768-8
Beigi GLiu H(2020)A Survey on Privacy in Social MediaACM/IMS Transactions on Data Science10.1145/33430381:1(1-38)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1145/3343038
Majeed ALee S(2020)Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing dataApplied Intelligence10.1007/s10489-020-01656-w50:8(2555-2574)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1007/s10489-020-01656-w
Karimi Rizi ANaderi Dehkordi MNemat bakhsh N(2019)SNISecurity and Communication Networks10.1155/2019/81712632019Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1155/2019/8171263
Li DLv QShang LGu N(2017)Efficient privacy-preserving content recommendation for online social communitiesNeurocomputing10.1016/j.neucom.2016.09.059219:C(440-454)Online publication date: 5-Jan-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.09.059
Wang SChen JTing IHong T(2016)(K, P)-shortest path algorithm in the cloud maintaining neighborhood privacyJournal of Web Engineering10.5555/3177203.317720515:1-2(29-44)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.5555/3177203.3177205
Li DChen CLv QShang LZhao YLu TGu N(2016)An algorithm for efficient privacy-preserving item-based collaborative filteringFuture Generation Computer Systems10.1016/j.future.2014.11.00355:C(311-320)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.future.2014.11.003
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