Towards identify anonymization in large survey rating data
Paper
Paper/Presentation Title | Towards identify anonymization in large survey rating data |
---|---|
Presentation Type | Paper |
Authors | Sun, Xiaoxun (Author) and Wang, Hua (Author) |
Journal or Proceedings Title | Proceedings of the 4th International Conference on Network and System Security (NSS 2010) |
ERA Conference ID | 43468 |
Article Number | 5636092 |
Number of Pages | 6 |
Year | 2010 |
Place of Publication | Piscataway, NJ. United States |
ISBN | 9780769541594 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/NSS.2010.11 |
Web Address (URL) of Paper | http://anss.org.au/nss2010/accepted.htm |
Conference/Event | NSS 2010: 4th International Conference on Network and System Security |
International Conference on network and System Security | |
Event Details | International Conference on network and System Security NSS Rank B B |
Event Details | NSS 2010: 4th International Conference on Network and System Security Parent International Conference on network and System Security Delivery In person Event Date 01 to end of 03 Sep 2010 Event Location Melbourne, Australia |
Abstract | We study the challenge of identity protection in the large public survey rating data. Even though the survey |
Keywords | identity protection; large survey rating data; data; privacy |
ANZSRC Field of Research 2020 | 460401. Cryptography |
460905. Information systems development methodologies and practice | |
460499. Cybersecurity and privacy not elsewhere classified | |
Public Notes | © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Department of Mathematics and Computing |
https://research.usq.edu.au/item/9zzw2/towards-identify-anonymization-in-large-survey-rating-data
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