Scope
Recent advances in computing and information technologies such as IoT, mobile Edge/Cloud computing, cyber- physical-social systems, Artificial Intelligence/Machine Learning/ Deep Learning, etc., have paved way for creating next generation smart and intelligent systems and applications that can have transformative impact in our society while accelerating rapid scientific discoveries and innovations. Such newer technologies and paradigms are getting increasingly embedded in the computing platforms and networked information systems/infrastructures that form the digital foundation for our personal, organizational and social processes and activities. It is increasingly becoming critical that the trust, privacy and security issues in such digital environments are holistically addressed to ensure the safety and well-being of individuals as well as our society.

IEEE TPS-ISA is an international multidisciplinary forum for presentation of state-of-the art innovations, and discussion among academic, industrial researchers, and practitioners on issues related to trust, privacy and security in emerging smart and intelligent systems and applications.


Topics
Topics of interest include, but are not limited to:

Foundational, theoretical models for trust, privacy and security in emerging applications
Trusted AI, Machine Learning and Deep Learning
Privacy preserving Machine Learning and Deep Learning
Trustworthy, safe and resilient intelligent systems
Trusted, privacy-conscious and secure systems, applications and networks/infrastructures
Security and privacy in IoT and Cyber-physical-human systems
Trustworthy and secure Human-Machine collaboration
Access and trust management/negotiation, and secure information flow/sharing
Bio-inspired approaches to trust, privacy and security
Game theoretical approaches to trust, privacy, and security
Adversarial machine learning
Trust, privacy and security for big data systems, applications and platforms
Trust, privacy and security for smart cities and urban computing
Machine Learning / Deep learning over encrypted data
Usability and human factors for trust, privacy and security
Tools, techniques and metrics for trust, privacy and security
Anonymization techniques and differential privacy for emerging intelligent applications
Trust, privacy and security approaches for services computing: microservices, service-oriented architectures, service composition and orchestration
Blockchain and Distributed-ledger technologies
Blockchain/Distributed ledger for e-commerce, mobile commerce and intelligent applications
Bias, fairness and integrity/robustness of algorithmic machine / AI algorithms
Trusted, privacy-aware and secure interoperation of interacting/collaborative systems
Threat models and attack modeling for AI/ML and applications
Identification/Detection of spam, phishing, malware and APTs
Cryptographic approaches and secure multiparty computation
Privacy-preserving data mining and big data analytics
Application of AI/ML and Deep learning for trust, privacy and security
Trust, privacy and security in edge/cloud computing, social computing
Safe and trusted autonomous vehicles/UAVs, robotics
Trust, security and safety in supply-chain environments and critical infrastructures
Data quality/credence, privacy and provenance
Trust in social media – disinformation/misinformation
Risk metrics and measurements, assessment/analysis and mitigation
Insider threat modeling, analysis and mitigation; behavioral modeling for security and trust
Digital payments and cryptocurrencies; Secure and trustworthy e-commerce and mobile commerce
Trust negotiation and/or propagation in interacting systems of systems, multi-agent systems, social networks.