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Counter Deanonymization Query: H-index Based k-Anonymization Privacy Protection for Social Networks

Authors:

Bo Song,

Xiaohua HuAuthors Info & Claims

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 809 - 812

https://doi.org/10.1145/3077136.3080649

Published: 07 August 2017 Publication History

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Abstract

In this paper, we propose a novel k-anonymization scheme to counter deanonymization queries on social networks. With this scheme, all entities are protected by k-anonymization, which means the attackers cannot re-identify a target with confidence higher than 1/k. The proposed scheme minimizes the modification on original networks, and accordingly maximizes the utility preservation of published data while achieving k-anonymization privacy protection. Extensive experiments on real data sets demonstrate the effectiveness of the proposed scheme, where the efficacy of the k-anonymized networks is verified with the distributions of pagerank, betweenness, and their Kolmogorov-Smirnov (K-S) test.

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Cited By

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Xia SChang BKnopf KHe YTao YHe XLi GLi ZIdreos SSrivastava D(2021)DPGraph: A Benchmark Platform for Differentially Private Graph AnalysisProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452756(2808-2812)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452756
Gao TLi FChen YZou X(2018)Local Differential Privately Anonymizing Online Social Networks Under HRG-Based ModelIEEE Transactions on Computational Social Systems10.1109/TCSS.2018.28770455:4(1009-1020)Online publication date: Dec-2018
https://doi.org/10.1109/TCSS.2018.2877045
Song BGao JDu HChen ZHu X(2018)Aligning Multiple PPI Networks with Representation Learning on Networks2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2018.8621084(136-141)Online publication date: Dec-2018
https://doi.org/10.1109/BIBM.2018.8621084

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Information

Published In

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2017

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Short-paper

Funding Sources

the National Science Foundation
the International Cooperation Projection of Hubei Province
the National Natural Science Foundation of China

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SIGIR '17

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SIGIR

SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all

Xia SChang BKnopf KHe YTao YHe XLi GLi ZIdreos SSrivastava D(2021)DPGraph: A Benchmark Platform for Differentially Private Graph AnalysisProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452756(2808-2812)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452756
Gao TLi FChen YZou X(2018)Local Differential Privately Anonymizing Online Social Networks Under HRG-Based ModelIEEE Transactions on Computational Social Systems10.1109/TCSS.2018.28770455:4(1009-1020)Online publication date: Dec-2018
https://doi.org/10.1109/TCSS.2018.2877045
Song BGao JDu HChen ZHu X(2018)Aligning Multiple PPI Networks with Representation Learning on Networks2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2018.8621084(136-141)Online publication date: Dec-2018
https://doi.org/10.1109/BIBM.2018.8621084

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