@inproceedings {INPROC-2018-15,
    author = {Christoph Stach and Sascha Alpers and Stefanie Betz and Frank D{\"u}rr and Andreas Fritsch and Kai Mindermann and Saravana Murthy Palanisamy and Gunther Schiefer and Manuela Wagner and Bernhard Mitschang and Andreas Oberweis and Stefan Wagner},
    title = {{The AVARE PATRON: A Holistic Privacy Approach for the Internet of Things}},
    booktitle = {Proceedings of the 15th International Conference on Security and Cryptography (SECRYPT '18)},
    publisher = {INSTICC Press},
    institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
    pages = {1--8},
    type = {Konferenz-Beitrag},
    month = {Juli},
    year = {2018},
    keywords = {Privacy; IoT Apps; Smart Things; Stream Processing; Privacy Preferences Elicitation \& Veri\&\#64257; cation},
    language = {Englisch},
    cr-category = {K.4.1 Computers and Society Public Policy Issues,
                   D.4.6 Operating Systems Security and Protection},
    contact = {Senden Sie eine E-Mail an Christoph.Stach@ipvs.uni-stuttgart.de},
    department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware;
                  Universit{\"a}t Stuttgart, Institut f{\"u}r Softwaretechnologie, Software Engineering;
                  Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Verteilte Systeme},
    abstract = {Applications for the Internet of Things are becoming increasingly popular. Due
      to the large amount of available context data, such applications can be used
      effectively in many domains. By interlinking these data and analyzing them, it
      is possible to gather a lot of knowledge about a user. Therefore, these
      applications pose a threat to privacy. In this paper, we illustrate this threat
      by looking at a real-world application scenario. Current state of the art
      focuses on privacy mechanisms either for Smart Things or for big data
      processing systems. However, our studies show that for a comprehensive privacy
      protection a holistic view on these applications is required. Therefore, we
      describe how to combine two promising privacy approaches from both categories,
      namely AVARE and PATRON. Evaluation results confirm the thereby achieved
      synergy effects.},
    url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2018-15&engl=0}
 }

@inproceedings {INPROC-2018-04,
    author = {Christoph Stach and Frank D{\"u}rr and Kai Mindermann and Saravana Murthy Palanisamy and Stefan Wagner},
    title = {{How a Pattern-based Privacy System Contributes to Improve Context Recognition}},
    booktitle = {Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (CoMoRea)},
    publisher = {IEEE Computer Society},
    institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
    pages = {1--6},
    type = {Workshop-Beitrag},
    month = {M{\"a}rz},
    year = {2018},
    keywords = {privacy; access control; pattern concealing; stream processing; complex event processing; databases},
    language = {Englisch},
    cr-category = {K.4.1 Computers and Society Public Policy Issues,
                   D.4.6 Operating Systems Security and Protection},
    contact = {Senden Sie eine E-Mail an Christoph.Stach@ipvs.uni-stuttgart.de},
    department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Anwendersoftware;
                  Universit{\"a}t Stuttgart, Institut f{\"u}r Softwaretechnologie, Software Engineering;
                  Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Verteilte Systeme},
    abstract = {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.},
    url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2018-04&engl=0}
 }