Topics include, but are not limited to, the following:

I. Design for Dependability and Security

    Architectures
        Fault-tolerant, secure, and safe middleware
        Intrusion detection and tolerance
        Firewall technologies; authentication and authorization of users, systems, and applications
        Network technologies, including protocols, routers, and switches
        On-line detection and recovery
    System Management and Administration
    Cryptography
        Application of cryptography techniques
        Key management
        Cryptographic protocols and algorithms, and their security
    Use of COTS components

II. Evaluation

    Modeling and Prediction
        Threat-assessment and intrusion-detection models
        Availability, performability, and survivability
        Intrusion tolerance; attack models
        Modeling methods, including Markov chains, queuing/fluid flow, Stochastic Petri Nets (SPN), SAN models
    Experimental Methods
        Monitoring and measurement
        Workload characterization
        Benchmarking
        Test-bed design, including fault, error, attack, and workload generation
        Field monitoring and measurement, including failures, security violations and break-ins, and intrusion detection
        Statistical methods
    Formal Specification and Verification
    Quality of Service (encompassing performability, security, and safety)

III. Application-Specific Areas

    Survivable Database Design and Transaction Processing
    Electronic Commerce
    Embedded Systems
    File and File System Security
    Distributed and Pervasive Systems
    Smartcards and Biometrics
    Real-Time Systems
    Safety-Critical Systems (e.g., aerospace computing)
    Internet Applications
    Multimedia Applications and Signal-Processing

IV. Software Design

    Operating System Support (for error/intrusion detection and/or tolerance)
    Self-Checking Software Systems
    Dependable, Secure Protocols and Network Interfaces
    Software Testing, Validation, and Verification
    Software Aging and Rejuvenation
    Software Security and Reliability Engineering

V. New Technologies (problems and opportunities)

    Nanoscale Computing
    Ad Hoc Networks
    Pervasive and Embedded Computing
    Networks of Satellites
    Data Mining
    Wearable Computers and Associated Networks
    Quantum Computing
    Cyber-Physical Systems (e.g., mobile health, Smart Grid, and ITS)

Special Issue on Security and Privacy in Federated Learning and Unlearning
Submission Date: 2026-03-31

Important dates 

    Submission Deadline: 31 March 2026
    First Review Due: May 31, 2026
    Revision Due: June 30, 2026
    Second Review Due / Notification: July 31, 2026
    Final Manuscript Due: August 31, 2026
    Publication Date: To be determined

The rise of distributed systems and pervasive computing has transformed how devices, systems, and services interact, creating a highly interconnected ecosystem. As the scale and complexity of these systems grow, issues related to security, privacy, and efficient management of decentralized resources become more pronounced. Addressing these challenges is critical to ensuring the security and privacy of user data, as well as compliance with regulatory standards in sensitive domains such as healthcare, finance, and beyond.

Federated Learning, a cutting-edge distributed learning paradigm, has emerged as a powerful tool for decentralized model training, enabling data privacy and reducing the need for central data aggregation. This method is pivotal in scenarios where data security and privacy are paramount.

Furthermore, Federated Unlearning—an innovative approach in the privacy-preserving space—focuses on the selective removal of data from models, which is essential for complying with data protection regulations and adapting to evolving privacy laws.

This special issue invites original contributions that explore the advanced technologies in Federated Learning and Unlearning, with an emphasis on their applications in security-sensitive environments. We welcome research papers that present novel methodologies, architectures, and strategies for enhancing the security, privacy, and efficiency of federated systems. All submissions will undergo peer review to ensure relevance and quality in line with the theme of this special issue.

Special Issue on AI Content Generation and Detection
Submission Date: 2026-04-30

Important dates 

    Submission deadline: 30 April 2026
    First-round decision notification: 20 July 2026
    Revised manuscript due: 30 August 2026
    Final decision notification: 20 October 2026

AI-generated contents have taken the world by storm. AI-generated contents now spans a wide range, including AI-generated videos, images, audios, and texts. Fueled by the accessibility of large-scale media datasets and the maturity of AI generation technologies, currently one may effortlessly create massive forgery images, videos, audios, and texts beyond human discernibility. These medias play important roles in filmmaking, electronic games, image editing, and education. It is thus not surprising that generative AI is redefining the video industry and many other industries. However, despite their positive widespread use, malicious actors can leverage advances in AI generation technologies for nefarious purposes. They may forge high-quality artifacts to perform scam, generate and propagate fake pornography, and challenge face recognition systems, to name a few. To alleviate the abuse of AI generation technologies, it is of paramount importance to develop sound detection and tracking approaches.

This special issue aims to bring together the cutting-edge advancements in AI content generation and detection, diving deeper into AI image generation, AI video generation, AI audio generation, AI text generation, and AI generated content detection tasks. We are interested in AI-Generated data covering a wide scope, from images, audios, texts, to videos. We expect the contributions focusing on innovative techniques for AI content generation and detection, including methodologies and algorithmic approaches to solve theoretical and practical problems. We also encourage the research on potentially impactful and related technologies.

The topics of interest for the Special Issue encompass, but are not limited to:

    AI-Driven Content Generation
    AI-Generated Content Detection
    Face Forgery Detection and Localization
    Cross-modal Content Generation 
    Human Motion Synthesis
    Explainability and Interpretability in AI Content Generation
    Privacy Preservation Techniques in AI-Generated Content
    Bias and Fairness in AI Generated Content
    Detection of Sensitive Elements in AI-Generated Content
    Content Verification and Authenticity Detection
    Safety Guardrails for Generative AI Models
    Synthesis Attribution and Reasoning
    Content Filtering and Moderation in Social Media Platforms
    AI-Generated Content Transmission Tracking
    Regulatory Frameworks and Legal Implications for AI Content Generation
    AI-Generated Content Watermarking
    AI-Generated Content Fingerprinting
    Adversarial Multimedia Forensics
    Multimedia Manipulation Detection
    AI-Generated Content Retrieval

Special Issue on Trustworthy and Private Decentralized Data Circulation
Submission Date: 2026-06-01

Important dates 

    Submission deadline: 1 June 2026
    First Review due: 1 September 2026
    Revision due: 1 November 2026
    Final decision notification: 1 December 2026
    Publication: To be determined

Data has become a critical factor of production, yet its circulation and value realization face fundamental challenges in trustworthiness, privacy, security, and regulatory compliance. Isolated techniques like encryption or anonymization are insufficient for complex real-world scenarios involving multi-party collaborative computing, data sovereignty assurance, circulation traceability, auditing, and reliability in adversarial environments. Recent advancements in privacy computing, such as Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), Differential Privacy (DP), Trusted Execution Environments (TEEs), and Federated Learning (FL)—combined with blockchain, offer novel paradigms and potentials for constructing next-generation trustworthy and private data circulation infrastructure.

This Special Issue aims to collate cutting-edge research on the deep integration of diverse privacy computing techniques with trustworthy computing mechanisms to address end-to-end trust and privacy preservation in cross-domain, cross-institutional data element circulation. We solicit high-quality original papers focusing on theoretical innovations, system building, practical deployment, and evaluation.

Topics of interest include:

    Trustworthy data collection for blockchain systems
    Privacy-preserving authentication for distributed systems
    Privacy-preserving data sharing among distributed users
    Privacy-preserving data computation between untrustworthy entities
    Privacy-preserving data auditing
    Secure and privacy-preserving data trading
    Secure data access control
    Secure and accurate data tracing
    Secure and efficient data governance
    Optimization of large-scale trustworthy and private data circulation systems
    Analysis of malware and cyberattacks for blockchain systems