Analysing Fairness of Privacy-Utility Mobility Models. (arXiv:2304.06469v1 [cs.LG])

Preserving the individuals' privacy in sharing spatial-temporal datasets is
critical to prevent re-identification attacks based on unique trajectories.
Existing privacy techniques tend to propose ideal privacy-utility tradeoffs,
however, largely ignore the fairness implications of mobility models and
whether such techniques perform equally for different groups of users. The
quantification between fairness and privacy-aware models is still unclear and
there barely exists any defined sets of metrics for measuring fairness in the
spatial-temporal context. In this work, we define a set …

attacks aware context critical datasets defined fairness human identification measuring metrics mobility privacy quantification sharing similarity techniques temporal utility work