posted by user: Aholzinger || 4578 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

ICDMML 2019   2019 International Conference on Data Mining and Machine Learning
ACML 2018   The 10th Asian Conference on Machine Learning
AMLTA 2019   The 4th International Conference on Advanced Machine Learning Technologies and Applications.
WSDM 2019   WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining
FI-IoTSP 2018   Future Internet Journal: Special Issue on IoT Security and Privacy
ISBDAI 2018   2018 International Symposium on Big Data and Artificial Intelligence
CSITS 2018   International Workshop on Cyber Security for Intelligent Transportation Systems
IJSCMC 2018   International Journal of Soft Computing, Mathematics and Control
QCAV 2019   Quality Control By Artificial Vision
ACIIDS 2018   10th Asian Conference on Intelligent Information and Database Systems