| Title | Deriving Privacy Settings for Location Sharing: Are Context Factors Always the Best Choice? |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Raber, Frederic, Krüger, Antonio |
| Conference Name | 2018 IEEE Symposium on Privacy-Aware Computing (PAC) |
| Keywords | adaptation, Current measurement, data privacy, fine-grained disclosement policy, location based services, location sharing, machine learning, Metrics, privacy, privacy models and measurement, privacy settings, pubcrawl, Social network services, social networking (online), social networks, Standards, text analysis, Urban areas, user modeling |
| Abstract | Research has observed context factors like occasion and time as influential factors for predicting whether or not to share a location with online friends. In other domains like social networks, personality was also found to play an important role. Furthermore, users are seeking a fine-grained disclosement policy that also allows them to display an obfuscated location, like the center of the current city, to some of their friends. In this paper, we observe which context factors and personality measures can be used to predict the correct privacy level out of seven privacy levels, which include obfuscation levels like center of the street or current city. Our results show that a prediction is possible with a precision 20% better than a constant value. We will give design indications to determine which context factors should be recorded, and how much the precision can be increased if personality and privacy measures are recorded using either a questionnaire or automated text analysis. |
| DOI | 10.1109/PAC.2018.00015 |
| Citation Key | raber_deriving_2018 |
Deriving Privacy Settings for Location Sharing: Are Context Factors Always the Best Choice?