TY  - CONF
AU  - Stach, Christoph
AU  - Dürr, Frank
AU  - Mindermann, Kai
AU  - Palanisamy, Saravana Murthy
AU  - Wagner, Stefan
A2  - Roussos, George
A2  - Kameas, Achilles
A2  - Hirmer, Pascal
A2  - Sztyler, Timo
A2  - Indulska, Jadwiga
T1  - How a Pattern-based Privacy System Contributes to Improve Context Recognition
T2  - Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops
PB  - IEEE
AD  - Athens
Y1  - 2018/march
SP  - 238
EP  - 243
M3  - https://doi.org/10.1109/PERCOMW.2018.8480227
KW  - privacy; access control; pattern concealing; stream processing; complex event processing; databases
N2  - As Smart Devices have access to a lot of user-preferential data, they come in handy in any situation. Although such data-as well as the knowledge which can be derived from it-is highly beneficial as apps are able to adapt their services appropriate to the respective context, it also poses a privacy threat. Thus, a lot of research work is done regarding privacy. Yet, all approaches obfuscate certain attributes which has a negative impact on context recognition and thus service quality. Therefore, we introduce a novel access control mechanism called PATRON. The basic idea is to control access to information patterns. For instance, a person suffering from diabetes might not want to reveal his or her unhealthy eating habit, which can be derived from the pattern "rising blood sugar level" -> "adding bread units". Such a pattern which must not be discoverable by some parties (e.g., insurance companies) is called private pattern whereas a pattern which improves an app's service quality is labeled as public pattern. PATRON employs different techniques to conceal private patterns and, in case of available alternatives, selects the one with the least negative impact on service quality, such that the recognition of public patterns is supported as good as possible.
ER  -