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Subject:
From:
Andreas Holzinger <[log in to unmask]>
Reply To:
Andreas Holzinger <[log in to unmask]>
Date:
Thu, 21 Jan 2016 08:21:25 +0100
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Call for Papers
Privacy Aware Machine Learning (PAML) for Health Data Science
Organized by Andreas HOLZINGER, Peter KIESEBERG, Edgar WEIPPL & A Min TJOA
in the context of ARES 2016 in Salzburg, August 29 – September, 2, 2016
Papers due to April, 1, 2016

http://hci-kdd.org/privacy-aware-machine-learning-for-data-science

Machine learning is the fastest growing field in computer science, and 
health informatics is among the greatest challenges, e.g. large-scale 
aggregate analyses of anonymized data can yield valuable insights 
addressing public health challenges and provide new starting points for 
scientific discovery. Privacy issues are becoming a major concern for 
machine learning tasks, which often operate on personal and sensitive 
data. Consequently, privacy, data protection, safety, information 
security and fair use of data is of utmost importance for health data 
science.

The amount of patient-related data produced in today’s clinical settings 
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 guaranteed 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 
trade-off 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 a 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?”

Research topics covered by this special session include but are not 
limited to the following topics:

– Production of Open Data Sets
– Synthetic data sets for machine learning algorithm testing
– Privacy preserving machine learning, data mining and knowledge discovery
– Data leak detection
– Data citation
– Differential privacy
– Anonymization and pseudonymization
– Securing expert-in-the-loop machine learning systems
– Evaluation and benchmarking

This special session will bring together scientists with diverse 
backgrounds, interested in both the underlying theoretical principles as 
well as the application of such methods for practical use in the 
biomedical, life sciences and health care domain. The cross-domain 
integration and appraisal of different fields will provide an atmosphere 
to foster different perspectives and opinions; it will offer a platform 
for novel crazy ideas and a fresh look on the methodologies to put these 
ideas into business.

Information about submission:
http://www.ares-conference.eu/conference/conference/submission/

-- 
Science is to test crazy ideas -
Engineering is to bring these ideas into Business
------------------------------------------------------------------------
Assoc.Prof. Dr.Andreas HOLZINGER, PhD, MSc, MPh, BEng, CEng, DipEd, MBCS
Holzinger Group, HCI-KDD, Lead Machine Learning CBmed Biomarker Research
Institute for Medical Informatics & Statistics, Medical University Graz
Auenbruggerplatz 2/V, A-8036 Graz (Austria)
Phone: ++43 316 385 13883, Fax: ++43 316 385 13590
http://hci-kdd.org
http://www.aholzinger.at/
Enjoy Thinking. Taming Information. Support Knowledge.
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