Location YardStick: Calculation of the Location Data Value Depending on the Users’ Context

Autor: Kota Tsubouchi, Junichi Sato, Kenta Kanamori, Tatsuru Higurashi
Rok vydání: 2020
Předmět:
Zdroj: IEEE BigData
DOI: 10.1109/bigdata50022.2020.9377812
Popis: These days, many apps acquire location data as a way of estimating the user’s behavior. As such, there are privacy concerns in using location data. In particular, users who are concerned about privacy may reduce the frequency of location acquisition or turn off the function, even though it degrades the quality of service. On the other hand, the only options available to users are yes-no or either-or ones such as "Always permit background acquisition" or "Permit only while using the app". For example, users who give permission to "Permit only while using the app" are themselves unable to understand how far their own veil of privacy will be lifted. That is, there are no metrics that can help users to understand the value of their own location data. How should the value of location data be determined? This study attempts to answer that question. The difficulty is that the value of a single point of location data depends on the context, such as how much other location data the app holds or when the location data was obtained. We propose a "Location YardStick" (LYS) that calculates the value of location information fairly in context. We confirmed that the LYS score is close to the user’s expectations by comparing its results with those of a large online survey of 1300 people, and we conducted case studies in which we calculated LYS on location data acquired in various actual contexts.
Databáze: OpenAIRE