// Securing Intelligence
Pioneering lightweight cryptographic systems and AI robustness frameworks at the intersection of quantum computing and machine learning security.
Developing next-generation cryptographic primitives that withstand both quantum computing threats and AI-powered adversarial attacks.
Designing and implementing NIST-compliant lattice-based and code-based cryptosystems optimized for resource-constrained environments.
Adversarial ML defense mechanisms, prompt injection mitigation, and cryptographic authentication for large language models.
Hardware-optimized ciphers (SAL, SHC, CSL) with FPGA/ASIC implementations for medical IoT and edge computing devices.
Hybrid deep learning architectures for CBCT analysis with HIPAA-compliant encryption and DNA-based long-term storage solutions.
Quantum-resistant consensus mechanisms and smart contract security auditing for next-generation distributed systems.
Exploring cryptographic principles in ancient texts and integrating traditional knowledge with modern post-quantum research.
VIT Bhopal University
Leading PQC research, PI for SPEAR Grant on LLM security
Symbiosis International University
Thesis: Lightweight Block Cipher with Pipelined Feistel Structure
SRTMU Nanded
Specialized in Information Security & Cryptography
45+ peer-reviewed publications in top-tier journals
3 patented lightweight cipher designs (SAL, SHC, CSL)
SPEAR Grant PI for LLM security research (₹5L+)
Trained IAS/IPS officers in cybersecurity via YASHADA
Incubated 3 tech startups in blockchain & IoT security
Recent contributions to cryptography and AI security
Novel S-box design achieving optimal nonlinearity with minimal hardware footprint for IoT devices. Published in IEEE Access with significant industry impact.
Pioneering work using neural networks to analyze cryptographic key schedules, revealing patterns invisible to traditional cryptanalysis methods.
A novel 64-bit block cipher with 128-bit key optimized for resource-constrained devices, demonstrating superior performance on Xilinx FPGAs.
Comprehensive analysis of implementing NIST PQC candidates on resource-constrained IoT platforms with practical benchmarks and optimization techniques.
Open to research collaborations, PhD supervision, industry consultations, and speaking engagements on cryptography and AI security.
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