-------- Weitergeleitete Nachricht -------- Betreff: [WI] CfP: Privacy Aware Machine Learning (PAML) for Health Data Science Datum: Mon, 18 Apr 2016 18:14:46 +0200 Von: Sven Wohlgemuth swohlge@live.de Antwort an: Sven Wohlgemuth swohlge@live.de An: wi@aifb.uni-karlsruhe.de Kopie (CC): Dr. Sven Wohlgemuth swohlge@live.de

I apologize should you receive multiple copies of this message.

============================== Call for Papers Special Session on Privacy Aware Machine Learning (PAML) for Health Data Science Keynote by Bernhard SCHÖLKOPF, Max Planck Institute for Intelligent Systems, Empirical Inference Department

within ARES / CD-ARES conference 2016 supported by the International Federation of Information Processing IFIP TC5, WG 8.4, and WG 8.9

This CfP addresses that k-anonymity is a NP-hard problem, which cannot be solved by automatic machine learning (aML) approaches. so that one needs to often make use of approximation and heuristics. It claims for interactive machine learning (iML) approaches and putting 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, 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.

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

Information about submission: http://cd-ares-conference.eu.dd21728.kasserver.com/?page_id=43

Please feel free to encourage interested people for submission and re-distribute this CfP.

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