Österreichische
Forschungsförderungsgesellschaft mbH (FFG

Mag.a Lisbeth Mosnik
Abteilung III/I 5 - Informations- und industrielle Technologien, Raumfahrt
Radetzkystraße 2, 1030 Wien
Tel.: +43 (0) 1 71162 Durchwahl 653414
Fax: + 43 (0) 1 71162 Durchwahl 652013

http://pptde.com/doc/888710/das-erwachen-der-macht-%E2%80%93-imagine-bits-of-tomorrow-lisbeth

My current research interests include data-centric applications in database and information systems with a particular focus on approximate matching techniques for complex data structures, efficient index structures for distance computations, and similarity search in massive data collections. My research is triggered by problems that arise in concrete applications, for example, e-government and XML search engines.

Allan Hanbury leitet eine Gruppe von Forschern an der TU Wien, welche sich mit der Suche und Analyse von unstrukturierten Daten beschäftigen. Zusätzlich ist er der Leiter des Data Science Research Studio von Research Studios Austria FG. Seine Hauptforschungsgebiete sind Information Retrieval und Textanalyse.

Er koordinierte bereits Forschungsprojekte aus den Bereichen Multimodal Information Retrieval und Information Retrieval Evaluation. Weiters war er als Leiter für drei EU-Projekte im Bereich der medizinischen Datenanalyse tätig (Khresmoi, VISCERAL und KConnect). Er koordiniert das FFG IKT Leitprojekt, Data Market Austria, ein Konsortium von 17 Unternehmen und Forschungszentren zur Gründung eines Datenservice-Ökosystems in Österreich.

Ruth Breu beschäftigt sich seit vielen Jahren mit der Frage, wie Unternehmen Anforderungen an Security, Qualität und Compliance im Kontext ihrer hochdynamischen IT-Landschaften effizient und effektiv erheben und überwachen können. In interdisziplinärer Arbeit liefert sie dabei anwendungs- und grundlagenorientierte Beiträge in den Bereichen Security Requirements Management, Enterprise Architecture Management und Model Engineering.

Ruth Breu ist seit 2002 Professorin an der Universität Innsbruck. Sie leitet dort die Forschungsgruppe Quality Engineering und seit 2013 das Institut für Informatik

Peter Kieseberg erhielt seinen Abschluss in „Technische Mathematik in den Computerwissenschaften“ an der TU Wien, seine Schwerpunkte lagen in den beriechen der Kryptographie, sowie der nummerischen Mathematik . Er arbeitete für einige Jahr als Consultant im Telekommunikationssektor bevor er zu SBA stieß. Seine hauptsächlichen Aktivitäten liegen im Bereich des Projektmanagements und der Projektgenerierung, sowie im Bereich der industrienahen und akademischen Forschung. Seit 2014 ist er außerdem visiting doctoral Researcher bei der Holzinger-Gruppe und involviert in Standardisierungsaktivitäten bei ETSI (TC-CYBER). Er ist Mitgründer der Firma Kibosec, die speziell im Bereich der Entwicklung von Services im Bereich der digitalen Forensik und fortgeschrittener Trainings tätig ist.

Seine hauptsächlichen Forschungsinteressen beinhalten verschiedene Themen der IT-Security: Digitale Forensik, speziell in Datenbanken, Fingerprinting von sensiblen Daten, sowie Mobile Security. Er ist Gründer und Organisator des IWSMA und derzeit First Vice Chair des IEEE-Chapters CS/SMC in Österreich.

Re­search In­terests

In­form­a­tion Ex­trac­tion

Un­su­per­vised Learn­ing

So­cial Me­dia

Open Data

Imagine 17 IKT der Zukunft Ideenmarathon
Raum K4, K5
Call for Participation
Mihai Lupu / RSA, Explaining Semantic search
Svitlana Vakulenko / WU, Talk Data To Me

Since January 2016 Claudia Plant is full professor for Data Mining at University of Vienna. Her research focuses on database-related data mining, especially clustering, parameter-free data mining, and integrative mining of heterogeneous data. She explores information-theoretic concepts for turning data into actionable knowledge with results being published at the top-level data mining conferences, e.g. KDD, ICDM and IEEE BigData and three Best Paper Awards. Results from interdisciplinary projects with experts from biology, neuroscience, and environmental engineering are have been published in leading journals such as Bioinformatics, Cerebral Cortex, and Water Research. In 2013, Claudia Plant obtained a Helmholtz Young Investigators Group, a German excellence program which offers funding for an independent research group. From 2010-2013 she has been visiting professor at Florida State University funded by a Feodor Lynen research fellowship of the Alexander von Humboldt Foundation. She spent further research visits at National University of Singapore and Carnegie Mellon University.

Publicly accessible databases are an indispensable resource for retrieving up to date infor- mation. But they also pose a significant risk to the privacy of the user, since a curious database operator can follow the user’s queries and infer what the user is after. Indeed, in cases where the users’ intentions are to be kept secret, users are often cautious about accessing the database. It can be shown that when accessing a single database, to completely guarantee the privacy of the user, the whole database should be down-loaded; namely n bits should be communicated (where n is the number of bits in the database). In this work, we investigate whether by replicating the database, more efficient solutions to the private retrieval problem can be obtained. We describe schemes that enable a user to access k replicated copies of a database ( k ≥ 2) and privately retrieve information stored in the database. This means that each individual server (holding a replicated copy of the database) gets no information on the identity of the item retrieved by the user. Our schemes use the replication to gain substantial saving. In particular, we present a two-server scheme with communication complexity O ( n 1 / 3).

This paper provides a short survey on transparency tools for
privacy purposes. It defines the term transparency tools, argues why they
are important and gives examples for transparency tools. A classification
of transparency tools is suggested and some example tools are analyzed
with the help of the classification

At the present time, an individual is required to reveal his identity when engaging in a wide range
of activities. Every time he uses a credit card, makes a telephone call, pays his taxes, subscribes to a
magazine, or buys something at the grocery store using a credit or debit card, an identifiable record
of each transaction is created and recorded in a computer database somewhere. In order to obtain a
service or make a purchase (using something other than cash), organizations require that you
identify yourself. This practice is so strong that it is simply treated as a given, an individual’s
identity must be collected and recorded in association with services rendered or purchases made.
But must this always be the case? Are there no situations where transactions may be conducted
anonymously, yet securely? We believe that there are, and will outline a number of methods and
technologies by which anonymous yet authentic transactions may be conducted.

As a legal concept, privacy is defined rather vaguely. That vagueness, some argue, is part of its protective function. The open-ended definition allows people to invoke privacy as a category to protect their personal lives and autonomy from intrusions by others—including the state that endows them with citizenship rights and runs surveillance programs. European Data Protection Directive (DPD) or Fair Information Practice Principles (FIPPs) on the other hand are procedural measures, such as notice and choice, data retention limitation, and subject access rights. These principles are seen to be instrumental to making the collection and processing activities of organizations transparent. Although less ambiguous, data protection principles still need to be translated into technical requirements and are vulnerable to narrow interpretations. Moreover, FIPPs fall short of mitigating all the privacy concerns of users toward a given organization. They also do not address privacy concerns users may have with respect to other users, with people in their social environments, and toward a greater public.