Research Contributions
Low-level systems research in Trusted Execution Environments (TEEs), constraint-based resource optimization, and resource-bounded Edge AI models.
PhD Thesis Objective & Open Questions
My primary research focus is building trustworthy, zero-trust intelligent infrastructure. In volatile cyber-physical grids (e.g. autonomous vehicles, border defense nodes), edge nodes run critical machine learning models in host environments that cannot be verified.
“How do we verify execution integrity and data confidentiality on remote edge devices subject to physical tampering, host compromises, and tight latency/power boundaries?”
By co-designing silicon-enforced memory enclaves (Intel SGX, ARM TrustZone), mathematical operations solvers (CP-SAT), and knowledge-distilled dynamic neural networks, my goal is to deliver platforms that operate securely in hostile edge environments.
Intel SGX Confidential Computing Backend
Hardware-Enforced Private Document Processing
System Architecture Topology
Summary
A hardware-isolated processing backend utilizing Intel SGX enclaves to protect memory-intensive document analysis pipelines from privileged cloud threats.
Problem Context
Deep learning and document analysis workloads deployed in third-party clouds are vulnerable to data leakage from compromise of the hypervisor, OS, or cloud administrators.
Research Motivation
Provide absolute cryptographic guarantees of confidentiality and runtime integrity for sensitive document pipelines using silicon-level isolation.
System Architecture
Untrusted Java Backend -> JNI Interface -> Secure Intel SGX C++ Enclave. Enclave memory boundaries configured to respect a ~16 MB trusted memory (EPC) footprint.
Applied Methods
Designed low-level ECALL/OCALL interfaces. Implemented AES-128-GCM authenticated encryption inside the enclave. Used memory paging/swapping to fit far more data than the enclave's native EPC capacity without breaking isolation.
Observed Results
Successfully processed workloads up to 2.86 GB of encrypted data on Azure Confidential VMs (DCsv2/DCsv3) inside a ~16 MB trusted enclave, with zero security degradation.
Future Directives
Explore transition to ARM TrustZone and RISC-V Keystone for mobile edge security topologies.
Technology Stack
GeoVision Deterrence System
Smart India Hackathon National Winner — Government R&D Category
5-Layer Universal Scheduling Engine
Operations Research at Loughborough University
Tactical Edge Wildlife Analytics
Knowledge Distillation on Raspberry Pi & ESP32
HawkEye UAV Platform
UAV Video Intelligence & Geospatial Detection Platform (Government R&D Program)
SpectraSam
Hyperspectral Crop/Land-Cover Classification Desktop App