|
|
 |
| |
|
|
|
A Survey of Privacy Preserving Data Publishing using Generalization and Suppression |
|
|
|
PP: 1103-1116 |
|
|
|
Author(s) |
|
|
|
Yang Xu,
Tinghuai Ma,
Meili Tang,
Wei Tian,
|
|
|
|
Abstract |
|
|
| Nowadays, information sharing as an indispensable part appears in our vision, bringing about a mass of discussions about
methods and techniques of privacy preserving data publishing which are regarded as strong guarantee to avoid information disclosure
and protect individuals’ privacy. Recent work focuses on proposing different anonymity algorithms for varying data publishing scenarios
to satisfy privacy requirements, and keep data utility at the same time. K-anonymity has been proposed for privacy preserving data
publishing, which can prevent linkage attacks by the means of anonymity operation, such as generalization and suppression. Numerous
anonymity algorithms have been utilized for achieving k-anonymity. This paper provides an overview of the development of privacy
preserving data publishing, which is restricted to the scope of anonymity algorithms using generalization and suppression. The privacy
preserving models for attack is introduced at first. An overview of several anonymity operations follow behind. The most important
part is the coverage of anonymity algorithms and information metric which is essential ingredient of algorithms. The conclusion and
perspective are proposed finally. |
|
|
|
|
|
|
|
