Generally, companies and governments both routinely collect and use our personal data. At the moment, our data and the way it is used is protected under relevant laws like GDPR in Europe or the US’ California Consumer Privacy Act (CCPA).
Data is ‘sampled’ and anonymised, which includes stripping the data of identifying characteristics like names and email addresses, so that individuals cannot, in theory, be identified. After this process, the data is no longer subject to data protection regulations, so it can be freely used and sold to third parties such as advertising companies and data brokers.
However, a white paper published in Nature Communications demonstrates that allowing data to be used, while preserving people’s privacy, requires much more than simply adding noise, sampling datasets, and other de-identification techniques.
The new research shows that once bought, the data can often be reverse engineered using machine learning to re-identify individuals, despite the anonymisation techniques. Moreover, the research demonstrates for the first time how easily and accurately this can be done, even with incomplete datasets, according to Science Daily.
In the research, 99.98% of Americans were correctly re-identified in any available ‘anonymised’ dataset by using just 15 characteristics, including age, gender, and marital status.
Overall, the researchers say policymakers must do more to protect individuals from such attacks, which could have serious ramifications for careers as well as personal and financial lives.
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