The field of secure computing and data protection is moving towards the development of more efficient and secure solutions for privacy-preserving applications. Researchers are focusing on designing novel attacks and defenses for various encryption schemes, such as substring-searchable symmetric encryption, and exploring new techniques for secure inference in cloud environments. Notably, there is a growing interest in leveraging hardware-based Trusted Execution Environments (TEEs) to secure data in use and protect against breaches. Additionally, advancements in biometric authentication and graph neural networks are being made to enhance flexibility, robustness, and security.

Noteworthy papers include: Leakage-abuse Attack Against Substring-SSE with Partially Known Dataset, which presents a novel matrix-based correlation technique to recover plaintext data from encrypted suffix tree structures. FLAME: Flexible and Lightweight Biometric Authentication Scheme in Malicious Environments, which proposes a hybrid approach combining lightweight secret-sharing-family primitives and two-party computation to achieve superior efficiency and security. PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks, which designs a lightweight cryptographic scheme for graph-centric inference in the cloud, achieving significant speedups over state-of-the-art solutions. Confidential Computing for Cloud Security: Exploring Hardware based Encryption Using Trusted Execution Environments, which explores the architecture and security features of TEEs and their effectiveness in improving cloud data security.