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Continuous Encryption Functions for Security Over Networks
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DOI：10.1016/j.sigpro.2022.108807.png)
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Abstract
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This paper presents a study of continuous encryption functions (CEFs) of secret feature vectors for secu-rity over networks, which include physical layer encryption for wireless communications and biometric template security for online Internet applications. While CEFs are defined here to include all prior contin-uous one-way functions, a good CEF is defined to be a continuous function that turns a random feature vector of limited dimension into a long sequence of numbers in such a way that it is hard to invert and hard to substitute, it has no or little amplification of noise, and its output samples have zero or near-zero correlations and have identical or nearly identical distributions. A number of prior CEFs, such as dynamic random projection, index-of-max hashing and higher-order polynomials, are all shown to fail on these criteria. Based on selected components of singular value decomposition (SVD) of randomly modulated matrices of the feature vector, a family of SVD-CEFs is proposed. Such a SVD-CEF is shown to meet all the criteria for a good CEF and outperform the prior CEFs significantly.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
One-way functions
Encryptions
Cancellable pass
Network security
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