LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method

Michael E. Houle; Vincent Oria; Kurt R. Rohloff; Arwa M. Wali

doi:10.1007/978-3-030-02224-2_11

LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method

Michael E. Houle, Vincent Oria, Kurt R. Rohloff, Arwa M. Wali

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

One of the most important information hiding techniques is fingerprinting, which aims to generate new representations for data that are significantly more compact than the original. Fingerprinting is a promising technique for secure and efficient similarity search for multimedia data on the cloud. In this paper, we propose LID-Fingerprint, a simple binary fingerprinting technique for high-dimensional data. The binary fingerprints are derived from sparse representations of the data objects, which are generated using a feature selection criterion, Support-Weighted Intrinsic Dimensionality (support-weighted ID), within a similarity graph construction method, NNWID-Descent. The sparsification process employed by LID-Fingerprint significantly reduces the information content of the data, thus ensuring data suppression and data masking. Experimental results show that LID-Fingerprint is able to generate compact binary fingerprints while allowing a reasonable level of search accuracy.

Original language	English (US)
Title of host publication	Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings
Editors	Stéphane Marchand-Maillet, Yasin N. Silva, Edgar Chávez
Publisher	Springer Verlag
Pages	134-147
Number of pages	14
ISBN (Print)	9783030022235
DOIs	https://doi.org/10.1007/978-3-030-02224-2_11
State	Published - 2018
Event	11th International Conference on Similarity Search and Applications, SISAP 2018 - Lima, Peru Duration: Oct 7 2018 → Oct 9 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11223 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	11th International Conference on Similarity Search and Applications, SISAP 2018
Country/Territory	Peru
City	Lima
Period	10/7/18 → 10/9/18

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Keywords

Fingerprinting
Information hiding
Intrinsic dimensionality
K-nearest neighbor graph

Access to Document

10.1007/978-3-030-02224-2_11

Cite this

Houle, M. E., Oria, V., Rohloff, K. R., & Wali, A. M. (2018). LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method. In S. Marchand-Maillet, Y. N. Silva, & E. Chávez (Eds.), Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings (pp. 134-147). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11223 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-02224-2_11

Houle, Michael E. ; Oria, Vincent ; Rohloff, Kurt R. et al. / LID-Fingerprint : A Local Intrinsic Dimensionality-Based Fingerprinting Method. Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings. editor / Stéphane Marchand-Maillet ; Yasin N. Silva ; Edgar Chávez. Springer Verlag, 2018. pp. 134-147 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c17a2ea254734f31a46b4920bc412f17,

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abstract = "One of the most important information hiding techniques is fingerprinting, which aims to generate new representations for data that are significantly more compact than the original. Fingerprinting is a promising technique for secure and efficient similarity search for multimedia data on the cloud. In this paper, we propose LID-Fingerprint, a simple binary fingerprinting technique for high-dimensional data. The binary fingerprints are derived from sparse representations of the data objects, which are generated using a feature selection criterion, Support-Weighted Intrinsic Dimensionality (support-weighted ID), within a similarity graph construction method, NNWID-Descent. The sparsification process employed by LID-Fingerprint significantly reduces the information content of the data, thus ensuring data suppression and data masking. Experimental results show that LID-Fingerprint is able to generate compact binary fingerprints while allowing a reasonable level of search accuracy.",

keywords = "Fingerprinting, Information hiding, Intrinsic dimensionality, K-nearest neighbor graph",

author = "Houle, {Michael E.} and Vincent Oria and Rohloff, {Kurt R.} and Wali, {Arwa M.}",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 11th International Conference on Similarity Search and Applications, SISAP 2018 ; Conference date: 07-10-2018 Through 09-10-2018",

year = "2018",

doi = "10.1007/978-3-030-02224-2_11",

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booktitle = "Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings",

address = "Germany",

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Houle, ME, Oria, V , Rohloff, KR & Wali, AM 2018, LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method. in S Marchand-Maillet, YN Silva & E Chávez (eds), Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11223 LNCS, Springer Verlag, pp. 134-147, 11th International Conference on Similarity Search and Applications, SISAP 2018, Lima, Peru, 10/7/18. https://doi.org/10.1007/978-3-030-02224-2_11

LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method. / Houle, Michael E.; Oria, Vincent ; Rohloff, Kurt R. et al.
Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings. ed. / Stéphane Marchand-Maillet; Yasin N. Silva; Edgar Chávez. Springer Verlag, 2018. p. 134-147 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11223 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - LID-Fingerprint

T2 - 11th International Conference on Similarity Search and Applications, SISAP 2018

AU - Houle, Michael E.

AU - Oria, Vincent

AU - Rohloff, Kurt R.

AU - Wali, Arwa M.

PY - 2018

Y1 - 2018

N2 - One of the most important information hiding techniques is fingerprinting, which aims to generate new representations for data that are significantly more compact than the original. Fingerprinting is a promising technique for secure and efficient similarity search for multimedia data on the cloud. In this paper, we propose LID-Fingerprint, a simple binary fingerprinting technique for high-dimensional data. The binary fingerprints are derived from sparse representations of the data objects, which are generated using a feature selection criterion, Support-Weighted Intrinsic Dimensionality (support-weighted ID), within a similarity graph construction method, NNWID-Descent. The sparsification process employed by LID-Fingerprint significantly reduces the information content of the data, thus ensuring data suppression and data masking. Experimental results show that LID-Fingerprint is able to generate compact binary fingerprints while allowing a reasonable level of search accuracy.

AB - One of the most important information hiding techniques is fingerprinting, which aims to generate new representations for data that are significantly more compact than the original. Fingerprinting is a promising technique for secure and efficient similarity search for multimedia data on the cloud. In this paper, we propose LID-Fingerprint, a simple binary fingerprinting technique for high-dimensional data. The binary fingerprints are derived from sparse representations of the data objects, which are generated using a feature selection criterion, Support-Weighted Intrinsic Dimensionality (support-weighted ID), within a similarity graph construction method, NNWID-Descent. The sparsification process employed by LID-Fingerprint significantly reduces the information content of the data, thus ensuring data suppression and data masking. Experimental results show that LID-Fingerprint is able to generate compact binary fingerprints while allowing a reasonable level of search accuracy.

KW - Fingerprinting

KW - Information hiding

KW - Intrinsic dimensionality

KW - K-nearest neighbor graph

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M3 - Conference contribution

AN - SCOPUS:85055104168

SN - 9783030022235

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 134

EP - 147

BT - Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings

A2 - Marchand-Maillet, Stéphane

A2 - Silva, Yasin N.

A2 - Chávez, Edgar

PB - Springer Verlag

Y2 - 7 October 2018 through 9 October 2018

ER -

Houle ME, Oria V , Rohloff KR, Wali AM. LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method. In Marchand-Maillet S, Silva YN, Chávez E, editors, Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings. Springer Verlag. 2018. p. 134-147. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-02224-2_11

LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method

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All Science Journal Classification (ASJC) codes

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