Scientific Publications
We conduct fundamental research in secure computing, cognitive architectures, and natural language modeling to unlock advanced engineering.
This paper introduces a mathematically verified framework for bounding side channel information leakage within hardware isolated systems. By modeling state transitions under differential analysis, we prove zero information leaks for target workloads, establishing a robust security paradigm for high integrity compute enclaves.
We document an architecture for connecting large scale neural network generators to decentralized workspace knowledge graphs.
We construct secure protocol definitions for executing collaborative data analysis pipelines without requiring participant nodes to decrypt raw input structures.
We construct mathematically verified proofs for validating row level access permissions on encrypted SQL columns without revealing metadata profiles.