Special Session 4:
Machine and Deep Learning in Privacy and Security
Recent developments in information and communication technologies (ICTs) are to require better analysis to secure information and systems in designing, developing, testing, understanding, accessing, processing, storing, sharing, analyzing, processing and training data in electronic media or platforms. The importance of data privacy and security increases all the time in diversity as well as in quantity because of continuous threats encountered in many new forms on internet, systems even in machine and deep learning platforms. In order to provide better solutions to secure and protect data in all media, new approaches, methods, techniques, technologies and concepts are always required.
Advances in Machine Learning (ML) and Deep Learning (DL) provide new challenges and solutions to information security and data privacy problems encountered in applications and theories. ML and DL have found widespread applications and implementations in security and privacy issues. Many ML and DL techniques, approaches, algorithms, methods, platforms and tools are extensively used by experts and researchers to achieve better results and to develop and design better and more robust and secure systems.
Scope and topics:
This special session invites submissions with new developments from those working in areas of machine and deep learning algorithms, systems and applications based on security and privacy perspectives. The scope of this special session is to:
- share the current and new research topics,
- bring researchers and experts together to discuss and share their experiences,
- enhance personal, enterprise, national and international awareness,
- increase the collaboration among university – industry – institutions,
- develop and design security and privacy aware systems, and
- protect systems from adversarial and cyber attacks
- design more reliable, trustworthy and secure AI systems
- provide a platform to present and discuss security and privacy issues in applications and studies using DL and ML methods.
Chairs: Seref SAGIROGLU
Bio: Prof. Dr. Seref Sagiroglu completed his undergraduate education in 1987 at Erciyes University, Department of Electronics Engineering. He completed his doctoral studies at the University of Wales College of Cardiff (now Cardiff University, UK) in 1994. He continues his academic career as the full professor in Software Engineering at Gazi University Computer Engineering Department. Sagiroglu has an outstanding academic with more than 8000 citation; almost 400 articles published in SCI/SSCI indexed journals, national and international conferences, symposium and workshops.
Technical Committee
- Dr. Seref SAGIROGLU (ss@gazi.edu.tr), Session Chair, Gazi University, Turkey
- Dr. Tiago A. ALMEIDA (talmeida@ufscar.br), Federal University of Sao Carlos, Brazil
- Dr. Cihan VAROL (cvarol@shsu.edu), Sam Houston State University, USA
- Dr. Maria Luisa SAPINO (mlsapino@di.unito.it), University of Torino, Italy
- Dr. Kittichai LAVANGNANANDA (Kitt@sit.kmutt.ac.th), King Mongkut's University of Technology, Tailand
- Dr. Yilmaz VURAL (yilmazvural@cusb.edu), California State University Santa Barbara,
- Dr. Suat OZDEMIR (suatozdemir@hacettepe.edu.tr)Hacettepe University, Turkey
Paper Submission Instructions
All papers will be double-blind reviewed and must present original work.
- CMT Submission Site
- Select the track: Special Session 4: Machine and Deep Learning in Privacy and Security
Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from:
Keydates
- Submission due date: September 5, 2023
- Notification of Acceptance: September 25, 2023
- Camera Ready Papers: October 5, 2023
- Pre-registration: October 15, 2023
- Conference: December 15-17, 2023
Registration
In order for your paper to be presented in the virtual session and published in the proceedings you must register to the conference.
Paper Presentation Instructions
The papers submitted to this track will be presented in person as part of the conference. There is no virtual presentation for this session.
ICMLA'23