We study the problem of anonymizing data with quasi-sensitive attributes. Quasi-sensitive attributes are not sensitive by themselves, but certain values or their combinations may ...
We extend the Camenisch-Lysyanskaya anonymous credential system such that selective disclosure of attributes becomes highly efficient. The resulting system significantly improves ...
Existing approaches on privacy-preserving data publishing rely on the assumption that data can be divided into quasi-identifier attributes (QI) and sensitive attribute (SA). This ...
Ada Wai-Chee Fu, Ke Wang, Raymond Chi-Wing Wong, Y...
The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to...
When disseminating data involving human subjects, researchers have to weigh in the requirements of privacy of the individuals involved in the data. A model widely used for enhancin...