https://puma.ub.uni-stuttgart.de/tag/stream_processing%20databasesPUMA RSS feed for /tag/stream_processing%20databases2026-04-22T13:24:16+02:00https://puma.ub.uni-stuttgart.de/bibtex/229482e95a9cbd13bb7a1762d7a0b2013/christophstachchristophstach2020-09-21T11:45:55+02:00access_control complex_event_processing databases pattern_concealing privacy stream_processing <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Stach" itemprop="url" href="/person/15a0424aefb2e6d7dc76587b78766c211/author/0"><span itemprop="name">C. Stach</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Frank Dürr" itemprop="url" href="/person/15a0424aefb2e6d7dc76587b78766c211/author/1"><span itemprop="name">F. Dürr</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kai Mindermann" itemprop="url" href="/person/15a0424aefb2e6d7dc76587b78766c211/author/2"><span itemprop="name">K. Mindermann</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Saravana Murthy Palanisamy" itemprop="url" href="/person/15a0424aefb2e6d7dc76587b78766c211/author/3"><span itemprop="name">S. Palanisamy</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefan Wagner" itemprop="url" href="/person/15a0424aefb2e6d7dc76587b78766c211/author/4"><span itemprop="name">S. Wagner</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshops</span>, </em></span><em>page <span itemprop="pagination">238–243</span>. </em><em>Athens, </em><em><span itemprop="publisher">IEEE</span>, </em>(<em><span>March 2018<meta content="March 2018" itemprop="datePublished"/></span></em>)</span>Mon Sep 21 11:45:55 CEST 2020AthensProceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshopsmar238–243CoMoRea '18How a Pattern-based Privacy System Contributes to Improve Context Recognition2018access_control complex_event_processing databases pattern_concealing privacy stream_processing 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.