Abstract
The wild enthusiasm for big data and open data has brought with it the assumptions that the utility of data-sets is what matters, and that privacy interests are to be sacrificed for the greater good. As a result, techniques have been devised to reduce the identifiability of expropriated data-records, on the assumption that privacy is to be compromised to the extent necessary. This paper argues for and adopts data privacy as the objective, and treats data utility for secondary purposes as the constraint. The inadequacies of both the concept and the implementation of de-identification are underlined. Synthetic data and Known Irreversible Record Falsification (KIRF) are identified as the appropriate techniques to protect against harm arising from expropriated data-sets.
Recommended Citation
Clarke, Roger, "Beyond De-Identification Record Falsification to Disarm Expropriated Data-Sets" (2019). BLED 2019 Proceedings. 53.
https://aisel.aisnet.org/bled2019/53