Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Zhouxuan Teng"'
Autor:
Zhouxuan Teng, Wenliang Du
Publikováno v:
Knowledge and Information Systems. 19:133-157
In this paper, we propose a hybrid multi-group approach for privacy preserving data mining. We make two contributions in this paper. First, we propose a hybrid approach. Previous work has used either the randomization approach or the secure multi-par
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c29389b3a9000b74953540f1eade35c8
https://doi.org/10.1007/s10115-008-0158-y
https://doi.org/10.1007/s10115-008-0158-y
Publikováno v:
SIGCSE
To provide students with hands-on exercises in computer security education, we have developed a laboratory environment (SEED) for computer security education. It is based on VMware, Minix, and linux, all of which are free for educational uses. Based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a309a00af91bc0816ccacdcdb2d3bfd
https://doi.org/10.1145/1227504.1227474
https://doi.org/10.1145/1227504.1227474
Publikováno v:
ICDM
Privacy-Preserving Data Re-publishing (PPDR) deals with publishing microdata in dynamic scenarios. Due to privacy concerns, data must be disguised before being published. Research in privacy-preserving data publishing (PPDP) has proposed many such me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7fb2931cf8a9c692bf7e236084c11951
https://doi.org/10.1109/icdm.2008.118
https://doi.org/10.1109/icdm.2008.118
Publikováno v:
SIGMOD Conference
Privacy-Preserving Data Publishing (PPDP) deals with the publication of microdata while preserving people' private information in the data. To measure how much private information can be preserved, privacy metrics is needed. An essential element for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d53041a8428c308e2acff7345df9de54
https://doi.org/10.1145/1376616.1376665
https://doi.org/10.1145/1376616.1376665
Publikováno v:
Knowledge Discovery in Databases: PKDD 2007 ISBN: 9783540749752
PKDD
PKDD
To preserve user privacy in Privacy-Preserving Data Mining (PPDM), the randomized response (RR) technique is widely used for categorical data. Although various RR schemes have been proposed, there is no study to systematically compare them in order t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60d51ba18103a37f4bb110c3f54eec9b
https://doi.org/10.1007/978-3-540-74976-9_50
https://doi.org/10.1007/978-3-540-74976-9_50
Autor:
Zhouxuan Teng, Wenliang Du
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783540717003
PAKDD
PAKDD
In this paper, we study the privacy-preserving decision tree building problem on vertically partitioned data. We made two contributions. First, we propose a novel hybrid approach, which takes advantage of the strength of the two existing approaches,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3cb57439d00f7577096f0998208781ff
https://doi.org/10.1007/978-3-540-71701-0_30
https://doi.org/10.1007/978-3-540-71701-0_30
Autor:
Zhouxuan Teng, Wenliang Du
Publikováno v:
ICDM
Recently K-anonymity has gained popularity as a privacy quantification against linking attacks, in which attackers try to identify a record with values of some identifying attributes. If attacks succeed, the identity of the record will be revealed an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::80e3e0345c44582f74fc79cba2f43c57
https://doi.org/10.1109/icdm.2006.40
https://doi.org/10.1109/icdm.2006.40
Autor:
Robert F. Erbacher, Zhouxuan Teng
Publikováno v:
Visualization and Data Analysis
The need to monitor today's networked computer systems for security purposes is a major concern. Our monitoring environment aids system administrators in keeping track of the activities on such systems with much lower time requirements than that of p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::835ecb42100e7a55831bc4d776af87e8
https://doi.org/10.1117/12.473927
https://doi.org/10.1117/12.473927
Publikováno v:
2008 Eighth IEEE International Conference on Data Mining; 2008, p1079-1084, 6p
Autor:
Carbonell, Jaime G., Siekmann, Jörg, Zhi-Hua Zhou, Hang Li, Qiang Yang, Zhouxuan Teng, Wenliang Du
Publikováno v:
Advances in Knowledge Discovery & Data Mining; 2007, p296-307, 12p