Fourier Magnitude-Based Privacy-Preserving Clustering on Time-Series Data
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.6 pp.1648-1651
- Publication Date
- 2010/06/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.1648
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Keyword
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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},}
Copy
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 -
English
