Publication
CISS 2016
Conference paper

Fidelity loss in distribution-preserving anonymization and histogram equalization

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Abstract

In this paper, we show a formal equivalence between histogram equalization and distribution-preserving quantization. We use this equivalence to connect histogram equalization to quantization for preserving anonymity under the k-anonymity metric, while maintaining distributional properties for data analytics applications. Finally, we make connections to mismatched quantization. These relationships allow us to characterize the loss in mean-squared error (MSE) performance of privacy-preserving quantizers that must meet distribution-preservation constraints as compared to MSE-optimal quantizers in the high-rate regime. Thus, we obtain a formal characterization of the cost of anonymity.

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CISS 2016

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