Motivation

Privacy is an increasingly important topic for information and communication technologies. Massive amounts of data are collected from the users' online activities and mobile device usage, and aggregated to form information which can affect a user's privacy. Although mechanisms exist to control access to such information, it is mostly impossible for a common user to understand and control the information which can be extracted from collected data. Location information is of particular concern in this context, as from seemingly anonymous location data vast amounts of information can be inferred.

Aim of the Project

Privacy preferences that users have when using location-based systems can typically be measured on a dichotomous level: either the users share their location in a certain situation or they do not. However, the users' actual privacy preferences are likely more fine-grained than this. It is the goal of this research project to

1. Understand and quantify such preferences by assessing fine-grained privacy preferences by a method of quantified value assignment

2. Understand how they relate to location sharing behaviour by empirically studying how this quantified location privacy valuation is reflected in actual privacy behaviour, taking the users' knowledge and awareness of location sharing into account

3. Make this knowledge accessible through formal modelling by adapting existing behavioural models to formalize privacy-related behaviour in a mobile context

Time Frame: 02.2020 – 09.2022

T-Labs Team Members: Vera Schmitt

Funding by: Deutsche Forschungsgemeinschaft (DFG)

Project Number: 409241470