IEICE TRANSACTIONS on Information
Privacy Protection by Matrix Transformation
Summary :
Privacy preserving is indispensable in data mining. In this paper, we present a novel clustering method for distributed multi-party data sets using orthogonal transformation and data randomization techniques. Our method can not only protect privacy in face of collusion, but also achieve a higher level of accuracy compared to the existing methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.4 pp.740-741
- Publication Date
- 2009/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.740
- Type of Manuscript
- LETTER
- Category
- Data Mining
Authors
Keyword
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Weijia YANG, "Privacy Protection by Matrix Transformation" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 4, pp. 740-741, April 2009, doi: 10.1587/transinf.E92.D.740.
Abstract: Privacy preserving is indispensable in data mining. In this paper, we present a novel clustering method for distributed multi-party data sets using orthogonal transformation and data randomization techniques. Our method can not only protect privacy in face of collusion, but also achieve a higher level of accuracy compared to the existing methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.740/_p
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@ARTICLE{e92-d_4_740,
author={Weijia YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Privacy Protection by Matrix Transformation},
year={2009},
volume={E92-D},
number={4},
pages={740-741},
abstract={Privacy preserving is indispensable in data mining. In this paper, we present a novel clustering method for distributed multi-party data sets using orthogonal transformation and data randomization techniques. Our method can not only protect privacy in face of collusion, but also achieve a higher level of accuracy compared to the existing methods.},
keywords={},
doi={10.1587/transinf.E92.D.740},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Privacy Protection by Matrix Transformation
T2 - IEICE TRANSACTIONS on Information
SP - 740
EP - 741
AU - Weijia YANG
PY - 2009
DO - 10.1587/transinf.E92.D.740
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2009
AB - Privacy preserving is indispensable in data mining. In this paper, we present a novel clustering method for distributed multi-party data sets using orthogonal transformation and data randomization techniques. Our method can not only protect privacy in face of collusion, but also achieve a higher level of accuracy compared to the existing methods.
ER -
English
