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

Document Type

Conference Proceeding

Publication Date

1-1-2018

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.

Identifier

85055104168 (Scopus)

ISBN

[9783030022235]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-030-02224-2_11

e-ISSN

16113349

ISSN

03029743

First Page

134

Last Page

147

Volume

11223 LNCS

Grant

AGS 1743321

Fund Ref

National Science Foundation

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