@inproceedings{d0eaf26827fe48bba1ca590e86342db6,
title = "Fidelity loss in distribution-preserving anonymization and histogram equalization",
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.",
keywords = "Data, Histogram equalization, K-anonymity, Mismatched quantization, Neural information processing",
author = "Varshney, {Lav R.} and Varshney, {Kush R.}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 50th Annual Conference on Information Systems and Sciences, CISS 2016 ; Conference date: 16-03-2016 Through 18-03-2016",
year = "2016",
month = apr,
day = "26",
doi = "10.1109/CISS.2016.7460471",
language = "English (US)",
series = "2016 50th Annual Conference on Information Systems and Sciences, CISS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "24--29",
booktitle = "2016 50th Annual Conference on Information Systems and Sciences, CISS 2016",
address = "United States",
}