A privacy-preserving hybrid middleware architecture that enforces a local trust boundary as its primary design constraint that is model-agnostic, requires no retraining of the underlying LLM, and is compatible with black-box API deployments is proposed and evaluated.
This study presents a detailed, actionable approach to constructing secure, privacy-focused RAG systems and culminates in the Integrated Privacy-Preserving RAG Framework (IPRAG), a five-tier architecture supported by a three-phase deployment protocol.
A privacy-preserving zk-SNARK-based audit framework that searches for probes designed in the spirit of adversarial examples to amplify logit drift between an approved model and a modified deployment and demonstrates that token-based probes consistently deliver the strongest mean sensitivity across models and GPU platfo...
This research investigates the security of large language models (LLMs) with the aim of identifying key security and safety threats, control measures, and governance considerations that are relevant to their trustworthy adoption in the Extended Reality (XR) domain. To achieve this goal, the research combines an extensi...
The growing use of generative artificial intelligence in enterprise and public-sector environments has introduced significant concerns regarding the protection of sensitive data. Generative AI systems process large volumes of information across training, fine-tuning, retrieval, inference, and output stages, creating mu...
A layered system model and an analysis pipeline that uses prior history as the temporal baseline, applies cumulative pruning and cross-proof propagation to each proof's Commitment Set, and recursively traces the survivors through historical hidden-state transitions to derive the final transaction-level Anonymity Set Si...
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