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.