Large language models (LLMs) have demonstrated proficiency in various language tasks but encounter difficulties in specific domain or scenario. These challenges are mitigated through prompt engineering techniques such as retrieval-augmented generation, which improves performance by integrating contextual information. However, concerns regarding the privacy implications of context-aware reasoning architectures persist, particularly regarding the transmission of sensitive data to LLMs service providers, potentially compromising personal privacy. To mitigate these challenges, this paper introduces Tex-tual Differential Privacy, a novel paradigm aimed at safeguarding user privacy in LLMs-based context-aware reasoning. The proposed Differential Embedding Hash algorithm anonymizes sensitive information while maintaining the reasoning capability of LLMs. Additionally, a quantification scheme for privacy loss is proposed to better understand the trade-off between privacy protection and loss. Through rigorous analysis and experimentation, the effectiveness and robustness of the proposed paradigm in mitigating privacy risks associated with context-aware reasoning tasks are demonstrated. This paradigm addresses privacy concerns in context-aware reasoning architectures, enhancing the trust and utility of LLMs in various applications.

Security infrastructures heavily rely on public key cryptography. With the emergence of quantum threats, NIST has been standardizing post-quantum cryptographic algorithms like CRYSTALS-Kyber. However, a major challenge in public-key cryptography is establishing a secure connection between verifiable information of specific entities and public keys. Traditionally, this challenge is addressed through Public Key Infrastructures (PKIs). Nevertheless, the current PKIs suffer from security and performance issues due to the requirement of central authorities. In this paper, we propose Inj-Kyber, a novel algorithm enhancing Kyber with information injection. Inj-Kyber achieves robust entity-key binding by injecting verifiable information into public keys while ensuring equivalent security to Kyber. Additionally, we showcase the applicability of Inj-Kyber through Bio-KEM, a KEM framework leveraging Inj-Kyber and biometric authentication to protect public keys via biometric information binding.

Online signature verification plays a pivotal role in security infrastructures. However, conventional online signature verification models pose significant risks to data privacy, especially during training processes. To mitigate these concerns, we propose a novel federated learning framework that leverages 1-D Convolutional Neural Networks (CNN) for online signature verification. Furthermore, our experiments demonstrate the effectiveness of our framework regarding 1-D CNN and federated learning. Particularly, the experiment results highlight that our framework 1) minimizes local computational resources; 2) enhances transfer effects with substantial initialization data; 3) presents remarkable scalability. The centralized 1-D CNN model achieves an Equal Error Rate (EER) of 3.33% and an accuracy of 96.25%. Meanwhile, configurations with 2, 5, and 10 agents yield EERs of 5.42%, 5.83%, and 5.63%, along with accuracies of 95.21%, 94.17%, and 94.06%, respectively.