LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method
LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method
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DOI：
10.1007/978-3-030-02224-2_11
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发表时间：
2018-10
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影响因子：
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通讯作者：
Michael E. Houle;Vincent Oria;Kurt Rohloff;Arwa M. Wali
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文献类型：
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作者：
Michael E. Houle;Vincent Oria;Kurt Rohloff;Arwa M. Wali
最重要的信息隐藏技术之一是指纹识别，其目的是为数据生成比原始数据更紧凑的新表示。指纹技术是一种很有前途的技术，用于云上多媒体数据的安全和高效的相似性搜索。在本文中，我们提出了LID-Fingerprint，一个简单的二进制指纹技术的高维数据。二进制指纹源自数据对象的稀疏表示，这些表示是使用相似性图构造方法NNWID-Descent中的特征选择标准支持加权固有维度（支持加权ID）生成的。LID-Fingerprint采用的稀疏化过程显着减少了数据的信息内容，从而确保数据抑制和数据掩蔽。实验结果表明，LID-Fingerprint能够生成紧凑的二进制指纹，同时允许合理的搜索精度。
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 proposeLID-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.