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.
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Acknowledgments
M. E. Houle acknowledges the financial support of JSPS Kakenhi Kiban (B) Research Grant 18H03296, and V. Oria acknowledges the financial support of NSF Research Grants DGE 1565478 and AGS 1743321.
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Houle, M.E., Oria, V., Rohloff, K.R., Wali, A.M. (2018). LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method. In: Marchand-Maillet, S., Silva, Y., Chávez, E. (eds) Similarity Search and Applications. SISAP 2018. Lecture Notes in Computer Science(), vol 11223. Springer, Cham. https://doi.org/10.1007/978-3-030-02224-2_11
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