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 proceedingConference contribution

1 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 languageEnglish (US)
Title of host publicationSimilarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings
EditorsStéphane Marchand-Maillet, Yasin N. Silva, Edgar Chávez
PublisherSpringer Verlag
Pages134-147
Number of pages14
ISBN (Print)9783030022235
DOIs
StatePublished - 2018
Event11th International Conference on Similarity Search and Applications, SISAP 2018 - Lima, Peru
Duration: Oct 7 2018Oct 9 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11223 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Similarity Search and Applications, SISAP 2018
Country/TerritoryPeru
CityLima
Period10/7/1810/9/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Fingerprinting
  • Information hiding
  • Intrinsic dimensionality
  • K-nearest neighbor graph

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