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Title:
Learning with Quantization, Polar Quantizer, and Secure Source Coding
Authors: Shanxiang Lyu, Ling Liu, Cong Ling
Abstract:This paper presents a generalization of the Learning With Rounding (LWR) problem, initially introduced by Banerjee, Peikert, and Rosen, by applying the perspective of vector quantization. In LWR, noise is induced by rounding each coordinate to the nearest multiple of a fraction, a process inherently tied to scalar quantization. By considering a new variant termed Learning With Quantization (LWQ), we explore large-dimensional fast-decodable lattices with superior quantization properties, aiming to enhance the compression performance over conventional scalar quantization. We identify polar lattices as exemplary structures, effectively transforming LWQ into a problem akin to Learning With Errors (LWE), where the distribution of quantization noise is statistically close to discrete Gaussian. Furthermore, we develop a novel ``quancryption’’ scheme for secure source coding. Notably, the scheme achieves near-optimal rate-distortion ratios for bounded rational signal sources, and can be implemented efficiently with quasi-linear time complexity. Python code of the polar-lattice quantizer is available at https://github.com/shx-lyu/PolarQuantizer.
ePrint: https://eprint.iacr.org/2024/714
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