Anupam Golder
Orcid: 0000-0003-0725-1593
According to our database1,
Anupam Golder authored at least 24 papers
between 2019 and 2026.
Collaborative distances:
Collaborative distances:
Timeline
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Bibliography
2026
Proceedings of the 39th International Conference on VLSI Design & 25th International Conference on Embedded Systems, 2026
HERACLES: 8192-Way SIMD Programmable Scalable Fully-Homomorphic Encryption SoC for Privacy-Preserving Cloud Computing in Intel 3 CMOS.
Proceedings of the IEEE International Solid-State Circuits Conference, 2026
Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2026
Proceedings of the ACM Asia Conference on Computer and Communications Security, 2026
2025
IEEE Des. Test, April, 2025
A 65-nm Delta-Sigma ADC-Based VDD-Variation-Tolerant Power-Side-Channel-Attack Sensor.
IEEE Solid State Circuits Lett., 2025
2024
Physical Side-Channel Vulnerability Assessment of Implementations of Cryptographic Algorithms.
PhD thesis, 2024
A 65nm Delta-Sigma ADC Based VDD-Variation-Tolerant Power-Side-Channel-Attack Monitor with Detection Capability Down to 0.25Ω.
Proceedings of the IEEE Symposium on VLSI Technology and Circuits 2024, 2024
Accurate Low-Degree Polynomial Approximation of Non-Polynomial Operators for Fast Private Inference in Homomorphic Encryption.
Proceedings of the Seventh Annual Conference on Machine Learning and Systems, 2024
Proceedings of the IEEE International Symposium on Hardware Oriented Security and Trust, 2024
2023
RAGA: Resource-Aware Tree-Splitting for High Performance Knuth-Yao-based Discrete Gaussian Sampling on FPGAs.
Proceedings of the Great Lakes Symposium on VLSI 2023, 2023
Power Side-Channel Vulnerability Assessment of Lightweight Cryptographic Scheme, XOODYAK.
Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023
PCB Identification Based on Machine Learning Utilizing Power Consumption Variability.
Proceedings of the 5th IEEE International Conference on Artificial Intelligence Circuits and Systems, 2023
2022
EM-X-DL: Efficient Cross-device Deep Learning Side-channel Attack With Noisy EM Signatures.
ACM J. Emerg. Technol. Comput. Syst., 2022
Exploration into the Explainability of Neural Network Models for Power Side-Channel Analysis.
Proceedings of the GLSVLSI '22: Great Lakes Symposium on VLSI 2022, Irvine CA USA, June 6, 2022
2021
EM and Power SCA-Resilient AES-256 Through >350× Current-Domain Signature Attenuation and Local Lower Metal Routing.
IEEE J. Solid State Circuits, 2021
2020
120.147 Efficient Electromagnetic Side Channel Analysis by Probe Positioning using Multi-Layer Perceptron.
IACR Cryptol. ePrint Arch., 2020
27.3 EM and Power SCA-Resilient AES-256 in 65nm CMOS Through >350× Current-Domain Signature Attenuation.
Proceedings of the 2020 IEEE International Solid- State Circuits Conference, 2020
Deep Learning Side-Channel Attack Resilient AES-256 using Current Domain Signature Attenuation in 65nm CMOS.
Proceedings of the 2020 IEEE Custom Integrated Circuits Conference, 2020
2019
Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack.
IEEE Trans. Very Large Scale Integr. Syst., 2019
Proceedings of the 56th Annual Design Automation Conference 2019, 2019