Activities per year
Abstract
Today’s increasingly complex information infrastructures represent the basis of any data-driven industries which are rapidly becoming the 21st century’s economic backbone. The sensitivity of those infrastructures to disturbances in their knowledge bases is therefore of crucial interest for companies, organizations, customers and regulating bodies. This holds true with respect to the direct provisioning of such information in crucial applications like clinical settings or the energy industry, but also when considering additional insights, predictions and personalized services that are enabled by the automatic processing of those data. In the light of new EU Data Protection regulations applying from 2018 onwards which give customers the right to have their data deleted on request, information processing bodies will have to react to these changing jurisdictional (and therefore economic) conditions. Their choices include a re-design of their data infrastructure as well as preventive actions like anonymization of databases per default. Therefore, insights into the effects of perturbed / anonymized knowledge bases on the quality of machine learning results are a crucial basis for successfully facing those future challenges. In this paper we introduce a series of experiments we conducted on applying four different classifiers to an established dataset, as well as several distorted versions of it and present our initial results
Original language | English |
---|---|
Title of host publication | Springer Lecture Notes in Computer Science LNCS 9817 |
Publisher | Springer International |
Pages | 251-266 |
Number of pages | 16 |
ISBN (Electronic) | 978-3-319-45507-5 |
ISBN (Print) | 978-3-319-45506-8 |
Publication status | Published - 3 Sept 2016 |
Event | Privacy Aware Machine Learning (PAML) for health data science - CD-ARES 2016, Salzburg, Austria Duration: 31 Aug 2016 → 3 Sept 2016 |
Conference
Conference | Privacy Aware Machine Learning (PAML) for health data science |
---|---|
Country/Territory | Austria |
City | Salzburg |
Period | 31/08/16 → 3/09/16 |
Keywords
- Machine Learning
- Health Informatics
- Privacy-Aware Machine Learning
ASJC Scopus subject areas
- Artificial Intelligence
Fields of Expertise
- Information, Communication & Computing
Treatment code (Nähere Zuordnung)
- Basic - Fundamental (Grundlagenforschung)
Fingerprint
Dive into the research topics of 'The right to be forgotten: Towards Machine Learning on perturbed knowledge bases'. Together they form a unique fingerprint.Activities
-
Privacy Aware Machine Learning (PAML)
Andreas Holzinger (Organiser)
1 Sept 2016Activity: Participation in or organisation of › Conference or symposium (Participation in/Organisation of)
-
Workshop Machine Learning for Biomedicine at TU Graz
Andreas Holzinger (Speaker)
26 Jan 2016Activity: Talk or presentation › Talk at workshop, seminar or course › Science to science