Large Language Models (LLMs) such as ChatGPT, Gemini, Claude, etc. have become integral tools for technical writing, software development, and communication. However, these systems, despite privacy assurances, still expose a significant privacy risk. User prompts often contain personally identifiable or proprietary information that is transmitted in plain-text to external providers, where it may be logged or accessed despite opt-out policies. This research aims to address this gap by designing a lightweight local encryption middleware that acts as a privacy firewall between users and remote AI APIs. Before a message is sent, the middleware automatically detects sensitive entities (Personally Identifiable Information or PII) and replaces them with typed placeholders such as [PERSON_1] or [ORG_1]. A local mapping (with optional authenticated encryption at rest) links these placeholders to their original values, allowing the system to reconstruct (rehydrate) the final response once the model replies. This design aims to reduce the exposure of sensitive information in human-readable form while maintaining the AI output. We evaluate the system in terms of detection accuracy, reconstruction fidelity, and utility preservation, demonstrating a practical solution for privacy-preserving LLM interaction.
Aditya Vishwakarma, Wencen Wu· International Conference on...· 0 citations
For decades, atomistic simulation of chemically complex Ni‐based superalloys has remained beyond practical reach. Here, we apply the GRACE foundational machine‐learning interatomic potential to predict chemical ordering and stacking‐fault energetics in the and phases of CMSX‐4, a commercial multicomponent Ni‐based superalloy. After benchmarking against structural and thermodynamic reference data, we use hybrid Monte‐Carlo/molecular dynamics sampling to study the impact of local chemical order on planar‐fault energies. GRACE reproduces elemental equilibrium lattice parameters within of DFT references, while underestimating melting temperatures of ordered Ni–Al phases by up to . The simulations reveal local chemical ordering in the phase and the expected sublattice occupancies in the phase. In the phase, the short‐range order raises the shear barriers by approximately while leaving the intrinsic stacking fault energy of unchanged. In the phase, alloying raises the complex and superlattice intrinsic stacking fault energies by approximately relative to stoichiometric Al. These results show that pretrained foundational potentials enable atomistic simulations of chemically complex multicomponent superalloys at scales inaccessible to direct first‐principles calculations.
Aditya Vishwakarma, Sarath Menon, Fritz Körmann et al.· Advanced Engineering Materia...· 0 citations