research-article

Negative databases for biometric data

Authors:

Julien Bringer,

Hervé ChabanneAuthors Info & Claims

MM&Sec '10: Proceedings of the 12th ACM workshop on Multimedia and security

Pages 55 - 62

https://doi.org/10.1145/1854229.1854242

Published: 09 September 2010 Publication History

Abstract

Negative databases -- negative representations of a set of data -- have been introduced in 2004 to protect the data they contain. Up to this day, there was no known solution to constitute biometric negative databases. This is surprising as biometric applications are very needy of such protection for privacy reasons. The main difficulty comes from the fact that biometric captures of the same trait give different results and comparisons of the stored reference with the fresh captured biometric data has to take into account this variability. In this paper, we give a first answer to this problem by exhibiting a way to create and exploit biometric negative databases.

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Cited By

Zhao DZhang PXiang JTian J(2022)NegDL: Privacy-preserving Deep Learning Based on Negative Database2022 4th International Conference on Data Intelligence and Security (ICDIS)10.1109/ICDIS55630.2022.00026(118-126)Online publication date: Aug-2022
https://doi.org/10.1109/ICDIS55630.2022.00026
Arteche AAsher CBull CDare HDatey IElshoff EMahmoud M(2022)Data Approach to Biometrics in Cybersecurity with Related Risks2022 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI58124.2022.00187(1059-1066)Online publication date: Dec-2022
https://doi.org/10.1109/CSCI58124.2022.00187
Liu RWang XWang C(2022)An efficient two-factor authentication scheme based on negative databases: Experiments and extensionsApplied Soft Computing10.1016/j.asoc.2022.108558119(108558)Online publication date: Apr-2022
https://doi.org/10.1016/j.asoc.2022.108558
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Index Terms

Negative databases for biometric data

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM&Sec '10: Proceedings of the 12th ACM workshop on Multimedia and security

September 2010

264 pages

ISBN:9781450302869

DOI:10.1145/1854229

General Chair:
Patrizio Campisi
Università degli Studi Roma TRE, Rome, Italy
,
Program Chairs:
Jana Dittmann
Otto-von-Guericke University Magdeburg, Germany
,
Scott Craver
SUNY Binghamton, USA

Copyright © 2010 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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2010

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Research-article

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MM&Sec '10

Sponsor:

SIGMM

MM&Sec '10: Multimedia and Security Workshop

September 9 - 10, 2010

Roma, Italy

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Overall Acceptance Rate 128 of 318 submissions, 40%

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Cited By

Zhao DZhang PXiang JTian J(2022)NegDL: Privacy-preserving Deep Learning Based on Negative Database2022 4th International Conference on Data Intelligence and Security (ICDIS)10.1109/ICDIS55630.2022.00026(118-126)Online publication date: Aug-2022
https://doi.org/10.1109/ICDIS55630.2022.00026
Arteche AAsher CBull CDare HDatey IElshoff EMahmoud M(2022)Data Approach to Biometrics in Cybersecurity with Related Risks2022 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI58124.2022.00187(1059-1066)Online publication date: Dec-2022
https://doi.org/10.1109/CSCI58124.2022.00187
Liu RWang XWang C(2022)An efficient two-factor authentication scheme based on negative databases: Experiments and extensionsApplied Soft Computing10.1016/j.asoc.2022.108558119(108558)Online publication date: Apr-2022
https://doi.org/10.1016/j.asoc.2022.108558
Zhang MLiao H(2020)Privacy-Preserving DBSCAN Clustering Algorithm Based on Negative Database2020 5th IEEE International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA49040.2020.9101288(209-213)Online publication date: May-2020
https://doi.org/10.1109/ICBDA49040.2020.9101288
Luo WHu YJiang HWang J(2019)Authentication by Encrypted Negative PasswordIEEE Transactions on Information Forensics and Security10.1109/TIFS.2018.284485414:1(114-128)Online publication date: Jan-2019
https://doi.org/10.1109/TIFS.2018.2844854
Zhao DHu XXiong STian JXiang JZhou JLi H(2019)A Fine-grained Privacy-preserving k-means Clustering Algorithm Upon Negative Databases2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9003033(1945-1951)Online publication date: Dec-2019
https://doi.org/10.1109/SSCI44817.2019.9003033
Zhao DLuo WLiu RYue L(2018)Negative Iris RecognitionIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2015.250713315:1(112-125)Online publication date: 1-Jan-2018
https://doi.org/10.1109/TDSC.2015.2507133
Liu RWang XWang C(2018)A Two-Factor Authentication Scheme based on Negative Databases2018 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2018.8628732(1110-1115)Online publication date: Nov-2018
https://doi.org/10.1109/SSCI.2018.8628732
Hu XLu LZhao DXiang JLiu XZhou HXiong STian J(2018)Privacy-Preserving K-Means Clustering Upon Negative DatabasesNeural Information Processing10.1007/978-3-030-04212-7_17(191-204)Online publication date: 17-Nov-2018
https://doi.org/10.1007/978-3-030-04212-7_17
Zhao DLuo W(2017)One-time password authentication scheme based on the negative databaseEngineering Applications of Artificial Intelligence10.1016/j.engappai.2016.11.00962:C(396-404)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.engappai.2016.11.009
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