The Right to Be Forgotten: Towards Machine Learning on Perturbed Knowledge Bases

  • Bernd Malle
  • Peter Kieseberg
  • Edgar Weippl
  • Andreas Holzinger
Conference paper

DOI: 10.1007/978-3-319-45507-5_17

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9817)
Cite this paper as:
Malle B., Kieseberg P., Weippl E., Holzinger A. (2016) The Right to Be Forgotten: Towards Machine Learning on Perturbed Knowledge Bases. In: Buccafurri F., Holzinger A., Kieseberg P., Tjoa A., Weippl E. (eds) Availability, Reliability, and Security in Information Systems. CD-ARES 2016. Lecture Notes in Computer Science, vol 9817. Springer, Cham

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.

Keywords

Machine learning Knowledge bases Right to be forgotten Perturbation Anonymization k-anonymity SaNGreeA Information loss Structural loss Cost weighing vector Interactive machine learning 

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Bernd Malle
    • 1
    • 2
  • Peter Kieseberg
    • 1
    • 2
  • Edgar Weippl
    • 2
  • Andreas Holzinger
    • 1
  1. 1.Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria
  2. 2.SBA Research gGmbHViennaAustria

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