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Detailed Introduction about the special sessions options:

1. Trustworthy and Verifiable Privacy-Preserving Computing

As data circulation becomes a cornerstone of the digital economy, the verifiability and trustworthiness of privacy-preserving computations have emerged as critical challenges. This special session focuses on advancing technologies that not only protect data privacy during processing but also provide cryptographic guarantees of computation correctness. We invite submissions on novel protocols and systems that enhance transparency and auditability in privacy computing, ensuring that data usage complies with agreed-upon policies even in untrusted environments. Topics of interest include verifiable computation, succinct proofs, and their integration with secure multi-party computation and homomorphic encryption.

2. Federated Learning and Edge Intelligence for Privacy-Preserving Analytics

Federated Learning (FL) has emerged as a paradigm-shifting approach to collaborative machine learning without centralizing raw data. This session delves into the latest advancements in FL, particularly its synergy with edge computing to enable low-latency, privacy-aware intelligence at the network periphery. We seek contributions that address the statistical, systems, and security challenges of FL, including handling non-IID data, communication efficiency, and robustness against poisoning attacks. The session also explores novel applications where federated and edge intelligence can unlock value from sensitive data in domains like healthcare, finance, and smart cities.

3. AI-Native Big Data Systems for Next-Generation Analytics

The complexity and scale of modern data demand a fundamental shift in how we architect data processing systems. This session explores the convergence of artificial intelligence and big data infrastructure, envisioning "AI-native" systems where machine learning models are not just a workload but an integral part of the data management and processing engine. We invite research on autonomous databases, learned indexing and query optimization, and intelligent resource scheduling. The goal is to foster discussion on how AI can drive unprecedented levels of efficiency, scalability, and automation in future big data platforms.

4. Generative AI, Data Governance, and Compliance

The rise of powerful generative AI models has created a critical tension between innovation and data rights. This session addresses the urgent challenges surrounding the use of potentially sensitive or copyrighted data in training and operating large language models (LLMs) and other generative models. We seek papers that explore technical and procedural solutions for ensuring data provenance, model transparency, and regulatory compliance. Topics of interest include machine unlearning, dataset de-duplication and attribution, and frameworks for responsible AI development that respect privacy and intellectual property.

5. Securing the IoT-Edge-Cloud Continuum with Big Data and Privacy Tech

The proliferation of Internet of Things (IoT) devices generates unprecedented volumes of data, creating a complex continuum from the edge to the cloud. Securing this data throughout its lifecycle—from collection on constrained devices to processing in centralized data centers—requires novel, integrated approaches. This session focuses on lightweight cryptographic protocols, scalable security analytics, and privacy-preserving techniques specifically designed for the resource-constrained and distributed nature of IoT systems. We invite contributions on topics ranging from blockchain-based device management to AI-driven threat detection across the continuum.

6. The Role of Privacy Computing in Cross-Domain Data Cooperation

Unlocking the value of data often requires collaboration across organizational boundaries, where data cannot be shared directly due to legal or competitive reasons. This session examines the pivotal role of privacy-computing technologies—such as secure multi-party computation (MPC), federated learning, and trusted execution environments—in enabling secure data cooperation and fusion. We seek papers that present real-world use cases, innovative protocols, and system designs that facilitate joint data analysis without exposing raw data. Application areas of interest include financial risk control, multi-institutional medical research, and smart city data sharing.

7. Post-Quantum Cryptography and the Future of Data Security

The advent of large-scale quantum computing poses a significant threat to the public-key cryptography that underpins much of our current data security and privacy infrastructure. This special session is dedicated to exploring the transition to post-quantum cryptography (PQC). We invite submissions on the standardization, implementation, and integration of quantum-safe algorithms into big data systems and privacy-preserving protocols. Discussions on the performance overhead of PQC, its impact on existing architectures, and the roadmap for a crypto-agile future are highly encouraged to prepare the community for the next era of data protection.