Scholarship

Publications

Selected publications in privacy-enhancing technologies, post-quantum cryptography, secure AI, computer architecture, and hardware security.

Selected Recent Publications

Structure-Aware Software Resilience for Number Theoretic Transforms.
Brittany Liu, Caroline Wang, Jayanta Chowdhury, Igor Nunes, Nahid Farhady, Elif Bilge Kavun, and Ro Cammarota.
FDTC, 2026.

GateBleed: Exploiting On-Core Accelerator Power Gating for High Performance & Stealthy Attacks on AI.
Joshua Kalyanapu, Farshad Dizani, Darsh Asher, Azam Ghanbari, Rosario Cammarota, Aydin Aysu, and Samira Mirbagher Ajorpaz.
IEEE Micro Top Picks, 2026.

FeatureBleed: Inferring Private Enriched Attributes From Sparsity-Optimized AI Accelerators.
Darsh Asher et al.
IEEE Computer Architecture Letters, 2026.

ASIC Tape-Out of the First Side-Channel Protected Neural Network Design.
Dubey et al.
IEEE Design & Test, 2026.

CPU too slow for FHE? Reach Real-Time Post-Quantum Secure Encrypted Computation with the HERACLES FHE Platform.
Race et al.
GOMACTech, 2026.

Secure and Efficient Neurosymbolic Reasoning with Controlled Homomorphic Encryption.
Swan Liu et al.
IEEE Transactions on Artificial Intelligence, 2026.

HERACLES: 8192-way SIMD Programmable Scalable Fully-Homomorphic Encryption SoC for Privacy-Preserving Cloud Computing in Intel 3 CMOS.
Golder et al.
IEEE ISSCC, 2026.

GateBleed: Exploiting On-Core Accelerator Power Gating for High Performance & Stealthy Attacks on AI.
Kalyanapu et al.
IEEE/ACM MICRO, 2025.

FHEmem: A Processing In-Memory Accelerator for Fully Homomorphic Encryption.
Zhou et al.
IEEE Transactions on Emerging Topics in Computing, 2025. 2026 Best Paper Runner-up.

Security Guidelines for Implementing Homomorphic Encryption.
Boussat et al.
IACR Cryptography in Context, 2025.

Private Detection of Relatives in Forensic Genomics using Homomorphic Encryption.
de Souza et al.
BMC Medical Genomics, 2025.

UFC: A Unified Accelerator for Fully Homomorphic Encryption.
Zhou et al.
IEEE/ACM MICRO, 2024.

Earlier Selected Work

Efficient Machine Learning on Encrypted Data using Hyperdimensional Computing.
Nam et al.
ISLPED, 2023.

Hardware-Software Co-design for Side-Channel Protected Neural Network Inference.
Dubey et al.
IEEE HOST, 2023.

ModuloNET: Neural Networks Meet Modular Arithmetic for Efficient Hardware Masking.
Dubey et al.
IACR Transactions on Cryptographic Hardware and Embedded Systems, 2022.

BioHD: An Efficient Genome Sequence Search Platform Using HyperDimensional Memorization.
Zou et al.
ISCA, 2022.

Guarding Machine Learning Hardware Against Physical Side-Channel Attacks.
Dubey et al.
ACM Journal on Emerging Technologies in Computing Systems, 2021.

AHEC: End-to-End Compiler Framework for Privacy-Preserving Machine Learning Acceleration.
Chen et al.
DAC, 2020.

CryptoPIM: In-Memory Acceleration for Lattice-Based Cryptographic Hardware.
Nejatollahi et al.
DAC, 2020.

nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data.
Boemer et al.
WAHC, 2019.

nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data.
Boemer et al.
Computing Frontiers, 2019.

Post-Quantum Lattice-Based Cryptography Implementations: A Survey.
Nejatollahi et al.
ACM Computing Surveys, 2019.

Complete Records

The publications above are a selected record of my work. For the complete and continuously updated bibliography, see DBLP and Google Scholar .