The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Clustering que preserva a privacidade (PPC em resumo) é importante na publicação de dados sensíveis de séries temporais. As soluções PPC anteriores, no entanto, têm o problema de não preservar pedidos à distância ou incorrer em violação de privacidade. Para resolver este problema, propomos uma nova abordagem PPC que explora magnitudes de Fourier de séries temporais. Nosso método baseado em magnitude não causa violação de privacidade, mesmo que suas técnicas ou parâmetros relacionados sejam revelados publicamente. Usar apenas magnitudes, entretanto, incorre no problema da ordem de distância e, portanto, apresentamos estratégias de seleção de magnitude para preservar tantas ordens de distância euclidianas quanto possível. Através de extensos experimentos, demonstramos a superioridade de nossa abordagem baseada em magnitude.
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Hea-Suk KIM, Yang-Sae MOON, "Fourier Magnitude-Based Privacy-Preserving Clustering on Time-Series Data" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 6, pp. 1648-1651, June 2010, doi: 10.1587/transinf.E93.D.1648.
Abstract: Privacy-preserving clustering (PPC in short) is important in publishing sensitive time-series data. Previous PPC solutions, however, have a problem of not preserving distance orders or incurring privacy breach. To solve this problem, we propose a new PPC approach that exploits Fourier magnitudes of time-series. Our magnitude-based method does not cause privacy breach even though its techniques or related parameters are publicly revealed. Using magnitudes only, however, incurs the distance order problem, and we thus present magnitude selection strategies to preserve as many Euclidean distance orders as possible. Through extensive experiments, we showcase the superiority of our magnitude-based approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1648/_p
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@ARTICLE{e93-d_6_1648,
author={Hea-Suk KIM, Yang-Sae MOON, },
journal={IEICE TRANSACTIONS on Information},
title={Fourier Magnitude-Based Privacy-Preserving Clustering on Time-Series Data},
year={2010},
volume={E93-D},
number={6},
pages={1648-1651},
abstract={Privacy-preserving clustering (PPC in short) is important in publishing sensitive time-series data. Previous PPC solutions, however, have a problem of not preserving distance orders or incurring privacy breach. To solve this problem, we propose a new PPC approach that exploits Fourier magnitudes of time-series. Our magnitude-based method does not cause privacy breach even though its techniques or related parameters are publicly revealed. Using magnitudes only, however, incurs the distance order problem, and we thus present magnitude selection strategies to preserve as many Euclidean distance orders as possible. Through extensive experiments, we showcase the superiority of our magnitude-based approach.},
keywords={},
doi={10.1587/transinf.E93.D.1648},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Fourier Magnitude-Based Privacy-Preserving Clustering on Time-Series Data
T2 - IEICE TRANSACTIONS on Information
SP - 1648
EP - 1651
AU - Hea-Suk KIM
AU - Yang-Sae MOON
PY - 2010
DO - 10.1587/transinf.E93.D.1648
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2010
AB - Privacy-preserving clustering (PPC in short) is important in publishing sensitive time-series data. Previous PPC solutions, however, have a problem of not preserving distance orders or incurring privacy breach. To solve this problem, we propose a new PPC approach that exploits Fourier magnitudes of time-series. Our magnitude-based method does not cause privacy breach even though its techniques or related parameters are publicly revealed. Using magnitudes only, however, incurs the distance order problem, and we thus present magnitude selection strategies to preserve as many Euclidean distance orders as possible. Through extensive experiments, we showcase the superiority of our magnitude-based approach.
ER -