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

2 Scopus citations


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
Number of pages14
ISBN (Print)9783030022235
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


Other11th International Conference on Similarity Search and Applications, SISAP 2018

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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


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