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Embedding Renewable Cryptographic Keys into Continuous Noisy Data

机译:将可更新的加密密钥嵌入到连续的嘈杂数据中

摘要

Fuzzy extractor is a powerful but theoretical tool to extract uniform strings from discrete noisy data. Before it can be used in practice, many concerns need to be addressed in advance, such as making the extracted strings renewable and dealing with continuous noisy data. We propose a primitive fuzzy embedder as a practical replacement for fuzzy extractor. Fuzzy embedder naturally supports renewability because it allows a randomly chosen string to be embedded. Fuzzy embedder takes continuous noisy data as input and its performance directly links to the property of the input data. We give a general construction for fuzzy embedder based on the technique of Quantization Index Modulation (QIM) and derive the performance result in relation to that of the underlying QIM. In addition, we show that quantization in 2-dimensional space is optimal from the perspective of the length of the embedded string. We also present a concrete construction for fuzzy embedder in 2-dimensional space and compare its performance with that obtained by the 4-square tiling method of Linnartz, et al. [13].
机译:模糊提取器是从离散的噪声数据中提取均匀字符串的强大但理论上的工具。在将其投入实际使用之前,需要预先解决许多问题,例如使提取的字符串可再生以及处理连续的嘈杂数据。我们提出了一种原始的模糊嵌入器,可以代替模糊提取器。模糊嵌入器自然支持可更新性,因为它允许嵌入随机选择的字符串。模糊嵌入器将连续的有噪数据作为输入,其性能直接链接到输入数据的属性。我们基于量化指数调制(QIM)技术给出了模糊嵌入器的一般构造,并得出了与基础QIM相关的性能结果。另外,从嵌入字符串的长度的角度来看,我们表明二维空间中的量化是最佳的。我们还提出了一种模糊嵌入器在二维空间中的具体构造,并将其性能与通过Linnartz等人的4平方平铺方法获得的性能进行了比较。 [13]。

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