Privacy Aware Machine Learning (PAML)

Holzinger, A. (Organiser)

Institute of Interactive Systems and Data Science (7060)

Activity: Participation in or organisation of › Conference or symposium (Participation in/Organisation of)

Description

Privacy Aware Machine Learning (PAML) for Health Data

Period	1 Sep 2016
Event type	Conference
Location	Salzburg, Austria

Treatment code (Nähere Zuordnung)

Basic - Fundamental (Grundlagenforschung)
Experimental

Fields of Expertise

Information, Communication & Computing

ASJC Scopus subject areas

Artificial Intelligence

Keywords

Machine Learning
Health Informatics

Documents & Links

The amount of patient-related data produced in today’s clinical setting poses many challenges with respect to collection, storage and responsible use. For example, in research and public health care analysis, data must be anonymized before transfer, for which the k-anonymity measure was introduced and successively enhanced by further criteria. As k-anonymity is an NP-hard problem, which cannot be solved by automatic machine learning (aML) approaches we must often make use of approximation and heuristics. As data security is not guranteed given a certain k-anonymity degree, additional measures have been introduced in order to refine results (l-diversity, t-closeness, delta-presence). This motivates methods, methodologies and algorithmic machine learning approaches to tackle the problem. As the resulting data set will be a tradeoff between utility, usability and individual privacy and security, we need to optimize those measures to individual (subjective) standards. Moreover, the efficacy of an algorithm strongly depends on the background knowledge of an potential attacker as well as the underlying problem domain. One possible solution is to make use of interactive machine learning (iML) approaches and put a human-in-the-loop where the central question remains open: “could human intelligence lead to general heuristics we can use to improve heuristics?”

Related content

Research Outputs
A Tamper-Proof Audit and Control-System for the Doctor in the Loop
Research output: Contribution to journal › Article › peer-review
The right to be forgotten: Towards Machine Learning on perturbed knowledge bases
Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Availability, Reliability, and Security in Information Systems: IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016, Proceedings
Research output: Book/Report › Book › peer-review