4th International Workshop on Advances on Privacy Preserving Technologies and Solutions

to be held in conjunction with the 19th International Conference on Availability, Reliability and Security

July 30 – August 2, 2024

The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in ML (ML) and artificial Intelligence (AI) and optimization applications. At the same time, it has increased exponentially the fertile threat landscape for cyber-attacks, skyrocketing the cyber risk of involved industries and impacting several areas, including computer vision, natural language processing, transportation, trust computing, identity management and psychological manipulation.

This workshop aims to strengthen security and privacy through research and relevant activities for the design of secure, privacy-preserving and trust architectures, investments in cyber-defense, data analyses, fusion platforms, protocols, algorithms, services, and applications for next generation systems and solutions. Security and privacy solutions employing innovative ML techniques are especially encouraged, to tackle the issues of large data volume inspection, cyberattacks, as well as theoretical and practical challenges for IoT platforms, particularly oriented to the design of privacy-preserving AI systems and algorithms. Moreover, such privacy-preserving AI systems and algorithms should have strong multidisciplinary components, including soliciting contributions about policy, legal issues, and societal impact of privacy and affect the cyber risk of the participating entities.

The 2024 IWAPS will bring together researchers, engineers, and practitioners for presenting and discussing the latest advances and innovations in theories, infrastructure, schemes, and applications for secure computation, privacy technologies, security economics, human computer interaction, as well as to identify emerging research topics and define the future trends.

Authors are invited to submit novel and unpublished work describing research or experience in all areas of privacy preserving and security technologies and solutions. A variety of research methods, including both qualitative and quantitative approaches will be considered within the scope of the workshop. Submitted papers will be judged based on their scientific quality, and contribution to the field.

The workshop is co-organized from the following European Commission projects:

  • AIAS (HORIZON, GA no: 101131292)
  • NITRO (DIGITAL, GA no: 101145872)
  • RESCALE (HORIZON, GA no: 101120962)
  • ERATOSTHENES (H2020, GA no: 101020416)
  • CYBERUNITY (DIGITAL, GA no: 101128024)
  • CHRISS (HORIZON, GA no: 101082440)
  • ENTRUST (HORIZON, GAS no: 101095634)
  • aerOS (HORIZON, GA no: 101069732)
  • COBALT (HORIZON, GA no: 101119602)
  • CyberSuite (DIGITAL, GA no: 101145861)
  • FAME (HORIZON, GA no: 101092639)
  • OASSES (HORIZON, GA no: 101092702)
  • ODEON (HORIZON, GA no: 101136128)
  • SAFE-6G (HORIZON, GA no: 101139031)
  • SOVEREIGN (HORIZON, GA no: 101131481)
  • TRUSTEE (HORIZON, GA no: 101070214)

Topics include, but are not limited to

Economic Implications of Adversarial AI
Ethical Considerations in Adversarial AI
Architectures and protocols for scalable, secure, robust and privacy enhancing technologies
Cryptographic approaches for security and privacy
Threat and attack models in IoT
End-to-end system security models for IoT
ML for security and privacy in privacy preserving technologies
ML technique for deep packet inspection
Privacy-preserving and machine-learning-based data analytics
ML technique to predict psychological manipulation
Game Theoretic approach to predict attacking paths
Privacy preserving security/privacy policies
Applications of privacy-preserving AI systems
Differential privacy: theory and applications
Human rights and privacy
Privacy policies and legal issues
Privacy preserving test cases and benchmarks
Security economics
AI/ML techniques in Cyber Threat Intelligence
Weakest link in Cybersecurity
ML in automated software testing

Human Factors in Adversarial AI
Adversarial AI in Cybersecurity
Ethical, psychological, sociological, or anthropological aspects of usable security and privacy
Trust frameworks and management models for IoT systems
Intrusion and malware detection for IoT systems
Deep Learning and privacy preserving
Protection solutions against adversarial ML attacks
ML to analyze cryptographic protocols
Analysis of mitigations and automating
ML in predicting the weakest link in an architecture
Privacy enhancing and anonymization techniques
Privacy preserving technologies/solutions for IoT systems
Attacks on data privacy
Distributed privacy-preserving algorithms
Security controls and budget allocation
Privacy preserving optimization and ML
Surveillance and societal issues
Investments in cyber-defense
Human firewall
Security and privacy frameworks
Cybersecurity risk management

Important Dates

Submission Deadline April 30, 2024
Author Notification May 17, 2024
Proceedings Version June 18, 2024
Conference July 30 – August 2, 2024

Workshop Chairs

Christos Xenakis
University of Piraeus, Greece

Aristeidis Farao
InQbit Innovation SRL, Romania

Technical Program Committee Chairs

Alexios Lekidis
University of Thessaly, Greece

Apostolis Zarras
Foundation for Research and Technology, Greece

Ilias Politis
ATHENA Research Centre, Greece

Chistoforos Dadoyan
Ionian University, Greece

Dissemination Chairs

Aggeliki Panou
University of Piraeus, Greece

Raisia Gorbunov
InQbit Innovation SRL, Romania

Programm Committee (tbc)


The submission guidelines valid for the workshop are the same as for the ARES conference. They can be found at https://www.ares-conference.eu/conference/submission/.