posted by user: Aholzinger || 5235 views || tracked by 8 users: [display]

PAML 2017 : Privacy Aware Machine Learning

FacebookTwitterLinkedInGoogle

Link: http://hci-kdd.org/privacy-aware-machine-learning-for-data-science-2
 
When Sep 1, 2017 - Sep 1, 2017
Where Reggio di Calabria
Submission Deadline Apr 1, 2017
Notification Due May 1, 2017
Final Version Due Jun 1, 2017
Categories    machine learning   privacy   open data   data science
 

Call For Papers

Machine learning is the fastest growing field in computer science [Jordan, M. I. & Mitchell, T. M. 2015. Machine learning: Trends, perspectives, and prospects. Science, 349, (6245), 255-260], and it is well accepted that health informatics is amongst the greatest challenges [LeCun, Y., Bengio, Y. & Hinton, G. 2015. Deep learning. Nature, 521, (7553), 436-444 ], e.g. large-scale aggregate analyses of anonymized data can yield valuable insights addressing public health challenges and provide new avenues for scientific discovery [Horvitz, E. & Mulligan, D. 2015. Data, privacy, and the greater good. Science, 349, (6245), 253-255]. Privacy is 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.
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 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 background, 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.

Accepted Papers will be published in a Springer Lecture Notes in Computer Science LNCS Volume.

Related Resources

MNLP 2020   4th IEEE Conference on Machine Learning and Natural Language Processing
ICDMML 2020   【EI SCOPUS】2020 International Conference on Data Mining and Machine Learning
WSPML 2020   2020 2nd International Workshop on Signal Processing and Machine Learning (WSPML 2020)
ICPR 2020   International Conference on Pattern Recognition 2020
AICA 2020   O'Reilly AI Conference San Jose
ICDM 2020   20th IEEE International Conference on Data Mining
Journal Special Issue 2019   Machine Learning on Scientific Data and Information
MAAIDL 2020   Springer Book 'Malware Analysis using Artificial Intelligence and Deep Learning'
SPTM 2020   8th International Conference of Security, Privacy and Trust Management
IEEE CiSt 2020   6th IEEE Congress on Information Science and Technology