To Reclaim Data Anonymity, Give Up The Proofs
Researcher Paul Francis tells Teb’s Lab why we might have to give up on mathematically certain methods of data anonymization in order to advance the field.
Privacy and data anonymity have been hot topics over the last few years. The adoption of regulations such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) as well as the challenges of getting such legislation right has spurred some of this interest. Scandals like the Cambridge Analytica kerfuffle that cost Facebook $5 billion dollars in FTC fines and the seemingly endless stream of data breaches have also driven public interest in data privacy.
Breaches and leaks are only one part of the conversation around privacy. In the case of a data breach, malicious actors obtain raw data complete with identifying features. Another part of the problem lies in anonymizing data that was always meant to be shared. In these cases the question is not so much how to prevent people from getting the data, but how to protect individual identities in the data sets that people receive. A chorus of voices have declared that data anonymity is almost impossible to achieve. I explored this perspective when I wrote Why ‘Anonymized Data’ Isn’t So Anonymous for OneZero in…
