Protection of user-defined sensitive attributes on online social networks against attribute inference attack via adversarial data mining

Jahid Reza; Md Zahidul Islam; Vladimir Estivill-Castro

doi:10.1007/978-3-030-49443-8_11

Protection of user-defined sensitive attributes on online social networks against attribute inference attack via adversarial data mining

Jahid Reza, Md Zahidul Islam, Vladimir Estivill-Castro

DTIC Universitat Pompeu Fabra

Research output: Book chapter/Published conference paper › Conference paper › peer-review

2 Citations (Scopus)

Abstract

Online social network (OSN) users share various types of personal information with other users. By analysing such personal information, a malicious data miner (or an attacker) can infer the sensitive information about the user which has not been disclosed publicly. This is generally known as attribute inference attack. In this study, we propose a privacy preserving technique, namely 3LP+, that can protect users’ multiple sensitive information from being inferred. We experimentally show that the 3LP+ algorithm can provide better privacy than an existing technique while maintaining the utility of users’ data.

Original language	English
Title of host publication	Information Systems Security and Privacy - 5th International Conference, ICISSP 2019, Revised Selected Papers
Editors	Paolo Mori, Steven Furnell, Olivier Camp
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	230-249
Number of pages	20
Volume	1221
ISBN (Electronic)	9783030494438
ISBN (Print)	9783030494421
DOIs	https://doi.org/10.1007/978-3-030-49443-8_11
Publication status	Published - 2020
Event	5th International Conference on Information Systems Security and Privacy: ICISSP 2019 - Vienna House Diplomat Prague, Prague, Czech Republic Duration: 23 Feb 2019 → 25 Feb 2019 https://icissp.scitevents.org/?y=2019 (Conference website) https://www.scitepress.org/ProceedingsDetails.aspx?ID=2JXfLZNuB94=&t=1 (proceedings) http://www.icissp.org/CallForPapers.aspx?y=2019 (call for papers) https://www.springer.com/gp/book/9783030494421 (Proceedings (Springer publication))

Publication series

Name	Communications in Computer and Information Science
Volume	1221 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	5th International Conference on Information Systems Security and Privacy
Country/Territory	Czech Republic
City	Prague
Period	23/02/19 → 25/02/19
Other	The International Conference on Information Systems Security and Privacy aims at creating a meeting point for researchers and practitioners that address security and privacy challenges that concern information systems, especially in organizations, including not only technological issues but also social issues. The conference welcomes papers of either practical or theoretical nature, presenting research or applications addressing all aspects of security and privacy, that concerns to organizations and individuals, thus creating new research opportunities.
Internet address	https://icissp.scitevents.org/?y=2019 (Conference website) https://www.scitepress.org/ProceedingsDetails.aspx?ID=2JXfLZNuB94=&t=1 (proceedings) http://www.icissp.org/CallForPapers.aspx?y=2019 (call for papers) https://www.springer.com/gp/book/9783030494421 (Proceedings (Springer publication))

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10.1007/978-3-030-49443-8_11

Cite this

Reza, J., Islam, M. Z., & Estivill-Castro, V. (2020). Protection of user-defined sensitive attributes on online social networks against attribute inference attack via adversarial data mining. In P. Mori, S. Furnell, & O. Camp (Eds.), Information Systems Security and Privacy - 5th International Conference, ICISSP 2019, Revised Selected Papers (Vol. 1221, pp. 230-249). (Communications in Computer and Information Science; Vol. 1221 CCIS). Springer. https://doi.org/10.1007/978-3-030-49443-8_11

Reza, Jahid ; Islam, Md Zahidul ; Estivill-Castro, Vladimir. / Protection of user-defined sensitive attributes on online social networks against attribute inference attack via adversarial data mining. Information Systems Security and Privacy - 5th International Conference, ICISSP 2019, Revised Selected Papers. editor / Paolo Mori ; Steven Furnell ; Olivier Camp. Vol. 1221 Cham, Switzerland : Springer, 2020. pp. 230-249 (Communications in Computer and Information Science).

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abstract = "Online social network (OSN) users share various types of personal information with other users. By analysing such personal information, a malicious data miner (or an attacker) can infer the sensitive information about the user which has not been disclosed publicly. This is generally known as attribute inference attack. In this study, we propose a privacy preserving technique, namely 3LP+, that can protect users{\textquoteright} multiple sensitive information from being inferred. We experimentally show that the 3LP+ algorithm can provide better privacy than an existing technique while maintaining the utility of users{\textquoteright} data.",

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Reza, J, Islam, MZ & Estivill-Castro, V 2020, Protection of user-defined sensitive attributes on online social networks against attribute inference attack via adversarial data mining. in P Mori, S Furnell & O Camp (eds), Information Systems Security and Privacy - 5th International Conference, ICISSP 2019, Revised Selected Papers. vol. 1221, Communications in Computer and Information Science, vol. 1221 CCIS, Springer, Cham, Switzerland, pp. 230-249, 5th International Conference on Information Systems Security and Privacy, Prague, Czech Republic, 23/02/19. https://doi.org/10.1007/978-3-030-49443-8_11

Protection of user-defined sensitive attributes on online social networks against attribute inference attack via adversarial data mining. / Reza, Jahid; Islam, Md Zahidul; Estivill-Castro, Vladimir.
Information Systems Security and Privacy - 5th International Conference, ICISSP 2019, Revised Selected Papers. ed. / Paolo Mori; Steven Furnell; Olivier Camp. Vol. 1221 Cham, Switzerland: Springer, 2020. p. 230-249 (Communications in Computer and Information Science; Vol. 1221 CCIS).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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AU - Islam, Md Zahidul

AU - Estivill-Castro, Vladimir

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N2 - Online social network (OSN) users share various types of personal information with other users. By analysing such personal information, a malicious data miner (or an attacker) can infer the sensitive information about the user which has not been disclosed publicly. This is generally known as attribute inference attack. In this study, we propose a privacy preserving technique, namely 3LP+, that can protect users’ multiple sensitive information from being inferred. We experimentally show that the 3LP+ algorithm can provide better privacy than an existing technique while maintaining the utility of users’ data.

AB - Online social network (OSN) users share various types of personal information with other users. By analysing such personal information, a malicious data miner (or an attacker) can infer the sensitive information about the user which has not been disclosed publicly. This is generally known as attribute inference attack. In this study, we propose a privacy preserving technique, namely 3LP+, that can protect users’ multiple sensitive information from being inferred. We experimentally show that the 3LP+ algorithm can provide better privacy than an existing technique while maintaining the utility of users’ data.

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KW - Data mining

KW - Online social networks

KW - Privacy protection technique

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Reza J, Islam MZ, Estivill-Castro V. Protection of user-defined sensitive attributes on online social networks against attribute inference attack via adversarial data mining. In Mori P, Furnell S, Camp O, editors, Information Systems Security and Privacy - 5th International Conference, ICISSP 2019, Revised Selected Papers. Vol. 1221. Cham, Switzerland: Springer. 2020. p. 230-249. (Communications in Computer and Information Science). Epub 2020 Jun 28. doi: 10.1007/978-3-030-49443-8_11

Protection of user-defined sensitive attributes on online social networks against attribute inference attack via adversarial data mining

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