Fair Context-Aware Privacy Threat Modelling

TL;DR

This paper explores fairness in privacy threat modeling, addressing the lack of methods to quantify fairness errors and considering how fairness varies across different contexts and causes of privacy threats.

Contribution

It introduces a novel examination of fairness notions in privacy threat models, highlighting the need for quantification methods in this area.

Findings

Identifies the absence of fairness quantification in privacy threat models

Analyzes fairness across different causes and contexts of privacy threats

Proposes a framework for fairness considerations in privacy threat modeling

Abstract

Given the progressive nature of the world today, fairness is a very important social aspect in various areas, and it has long been studied with the advent of technology. To the best of our knowledge, methods of quantifying fairness errors and fairness in privacy threat models have been absent. To this end, in this short paper, we examine notions of fairness in privacy threat modelling due to different causes of privacy threats within a particular situation/context and that across contexts.