Human intuition as a defense against attribute inference.

ABSTRACT: Attribute inference-the process of analyzing publicly available data in order to uncover hidden information-has become a major threat to privacy, given the recent technological leap in machine learning. One way to tackle this threat is to strategically modify one's publicly available data in order to keep one's private information hidden from attribute inference. We evaluate people's ability to perform this task, and compare it against algorithms designed for this purpose. We focus on three attributes: the gender of the author of a piece of text, the country in which a set of photos was taken, and the link missing from a social network. For each of these attributes, we find that people's effectiveness is inferior to that of AI, especially when it comes to hiding the attribute in question.

SUBMITTER: Waniek M 

PROVIDER: S-EPMC10522675 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature