Using Generalization Patterns for Fingerprinting Sets of Partially Anonymized Microdata in the Course of Disasters

In the event of large natural and artificial disasters, it is of vital importance to provide all sorts of data to the relief organizations (fire department, red cross,...) to enhance their effectivity. Still, some of this data (e.g. regarding personal information on health status) may be considered private. k-anonymity can be utilized to mitigate the risks resulting from disclosure of such data, however, sometimes it is not possible to achieve a suitable size for k in order to completely anonymize the data without interfering with rescue operations. Still, this data will be sensitive after the disaster recovery is finished. Thus we aim at protecting the data by devising an intrinsic fingerprinting-scheme that allows to detect the source of eventually disclosed information afterwards. Our approach uses the properties directly derived from the anonymization process to generate unique fingerprints for every data set.