posted by user: Aholzinger || 1195 views || tracked by 4 users: [display]



When Aug 27, 2018 - Aug 30, 2018
Where Hamburg
Submission Deadline Apr 30, 2018
Final Version Due Jun 27, 2018
Categories    federated machine learning   client-side computing   blockchain   privacy-by-design

Call For Papers

---begin call for papers---

*** CD-MAKE 2018 Workshop MAKE PRIVACY BY DESIGN ***

at the International IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction CD-MAKE
in Hamburg, August 27 – 30, 2018
in conjunction with the 13th international conference on availability, reliability and security (ARES 2018),

Submissions due to April, 30, 2018
Springer LNCS camera ready deadline (hard) June, 27, 2018

ORGANIZED by (in alphabetical order):

Jan BAUMBACH, Technical University Munich, DE
Dominik HEIDER, University of Marburg, DE
Andreas HOLZINGER, Medical University Graz, AT
Peter KIESEBERG, SBA-Research and University of Applied Sciences St.Poelten, AT
Richard ROETTGER, University of Southern Denmark, Odense, DK
Edgar WEIPPL, SBA-Research Vienna, AT


We encourage to submit original papers on novel techniques, new applications, advanced methodologies, promising research directions and discussions of unsolved future issues on, but not limited to:

Federated machine learning,
Federated learning with the human-in-the-loop,
Distributed learning,
Learning trust and reputation,
Client-side computing,
Privacy aware machine learning,
Collaborative privacy aware machine learning,
Blockchain security technologies,
Decentralized representation learning,
Secure feature sharing,
On-Device Artificial Intelligence




Increasing privacy concerns in the health domain (e.g. due to new European Data Protection Regulations) require new approaches in AI and machine learning. One problem of the health domain is, that heterogenous data sources are extremely distributed over different locations. Secure storage and sharing of sensitive health data is a big challenge and mostly prohibit open research cross-institutional, even cross-departmental. Current technologies face limitations regarding safety, security, privacy, data protection and ecosystem interoperability. Standard methods, e.g. sending sensitive health data into a cloud for analysis is meanwhile a no-go and not suitable in the future for a number of reasons. The problem is twofold: On the one hand hospitals need a secure platform to store sensitive data, but on the other hand any health research (e.g. cancer research) needs to be openly shared for global research. In the health informatics domain one possible future solution is to in federated machine learning – making use of client-side computing and latest blockchain technologies [1], [2]. The premise is NOT to share any data (!) – but to share the learned representations (features) where a lot of reserach is urgently needed in order to bring novel ideas into daily business. This approach is privacy-by-design.


This workshop brings together experts from diverse areas to pave the way for future collaborations in assessing and reducing cyber risks in hospitals and health care centers to help not only to protect sensitive patient privacy, but at the same time enable international open research on shared representations. The central goal is in improved security of health data, services and infrastructures with no risk of data privacy breaches and increased patient and researcher trust and safety in AI/machine learning approaches in open science. All papers will be peer reviewed by our international scientific conference committee:


CD-MAKE is a joint effort of IFIP TC 5 (Information Technology Applications), TC 12 (Artificial Intelligence), IFIP WG 8.4 (E-Business: Multi-disciplinary research and practice), IFIP WG 8.9 (Enterprise Information Systems) and IFIP WG 12.9 (Computational Intelligence) and is held in conjunction with the International Conference on Availability, Reliability and Security (ARES).

Goal of CD-MAKE: To act as a Catalyst to bring together researchers in an cross-disciplinary manner, to stimulate fresh ideas and to encourage multi-disciplinary problem solving in the area of AI and machine learning.

CD stands for Cross-Domain and means the integration and appraisal of seemingly disparate fields (e.g. algebraic topology, entropy, geometry, etc.) and different application domains (e.g. Health, Industry 4.0, AAL, etc.) to provide an atmosphere to foster different perspectives and opinions. The conference is dedicated to offer an international platform without any boundaries for novel ideas and a fresh look on the methodologies to put crazy ideas into Business for the benefit of society. Serendipity is a desired effect, and shall cross-fertilize methodologies and transfer of algorithmic developments.

MAKE stands for MAchine Learning & Knowledge Extraction.

---end of call for papers---

Related Resources

S&P 2022   IEEE Symposium on Security and Privacy (Third deadline)
ICBDB 2021   2021 3rd International Conference on Big Data and Blockchain(ICBDB 2021)
CODASPY 2022   The 12th ACM Conference on Data and Application Security and Privacy
SoCAV 2022   2022 International Symposium on Connected and Autonomous Vehicles (SoCAV 2022)
CEVVE 2022   2022 International Conference on Electric Vehicle and Vehicle Engineering (CEVVE 2022)
ACITY 2021   11th International Conference on Advances in Computing and Information Technology
AAAI-MAKE 2022   AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence
blockchain_ml_iot 2021   Network and Electronics (MDPI) Joint Special Issue - Blockchain and Machine Learning for IoT: Security and Privacy Challenges
IJSCAI 2021   International Journal on Soft Computing, Artificial Intelligence and Applications
IEEE CSP--EI Compendex, Scopus 2022   2022 IEEE 6th International Conference on Cryptography, Security and Privacy (CSP 2022)--EI Compendex, Scopus