Question

How do anonymized datasets fall short of their goal of being anonymous?

A: Anonymized datasets can be combined with other datasets, which can re-identify individuals
B: Anonymized datasets can be re-identifyed by anyone holding the hash key
C: Anonymized datasets can be traced back to the individuals by looking at their browsing history in the app
D: Anonymized datasets aren't actually anonymous because many of the data fields can identify a user

Answer 1

Anonymized datasets may fall short of true anonymity by allowing re-identification through combining with other datasets or analysis of detailed metadata which can reveal personal information.

Step-by-step explanation:

Anonymized datasets are designed to protect individual privacy by removing personal identifiers. However, they can fall short of being truly anonymous. One way is that anonymized datasets can be combined with other datasets, which may inadvertently lead to re-identification of individuals.

Unique patterns within anonymized datasets can end up serving as alternative identifiers when matched with additional data sources. Another potential shortfall is when anonymized datasets contain sufficient detail that permits entities to deduce user identities through matching activities, patterns, or metadata, which includes data about data (such as location or time stamps).

Although direct identifiers like names are removed, the remaining details may still reveal personal information if the datasets are not thoroughly vetted and safeguarded against such risks.