Ferhat Yaman

I am a Ph.D. student at NC State in Computer Engineering program, advised by Aydin Aysu. Before joining NC State, I completed B.Sc. degree in both Computer Science and Electronics Engineering at Sabanci University. I also completed a minor degree in Neuroscience.

My main research interests include homomorphic encryption(HE), post-quantum cryptography(PQC), and side-channel analysis(SCA).

Recently, I am working on the acceleration of attack algorithms of lattice cryptography. I am also working on the SCA of machine learning(ML) accelerators and trying to recover the secret ML models. Previously, I worked on software acceleration of HE by GPUs and privacy-preserving ML applications by using HE. I also designed a hardware accelerator for KYBER which is one of the final candidates in the PQC standardization process.

Publications

Hands-On Teaching of Hardware Security for Machine Learning

Calhoun, Ashley, Ortega, Erick, Yaman, Ferhat, Dubey, Anuj, and Aysu, Aydin

In Proceedings of the Great Lakes Symposium on VLSI 2022 2022

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Hardware security for machine learning (ML) and artificial intelligence (AI) circuits is becoming a major topic within the cybersecurity framework. Although much research is ongoing on this front, the community omits the educational components. In this paper, we present a training module comprised of a set of hands-on experiments that allow teaching hardware security concepts to newcomers. Specifically, we propose 5 experiments and related training material that teach side-channel attacks and defenses on the hardware implementations of neural networks. We report the organization and the findings after testing these experiments with sophomore undergraduate students at North Carolina State University. The students first study the basics of neural networks and then build a neural network inference circuit on a breadboard. They then conduct a differential power analysis attack on the hardware to steal trained weights and a circuit-balancing (hiding) style defense to mitigate the attack. The students develop all related hardware and software codes to perform attacks and build defenses. The results show that such complex notions of digital circuits design, neural networks, and side-channel analysis can be instructed at the sophomore level with a well-thought set of experiments. Future extensions could include establishing an online infrastructure for remote teaching and efficient scaling to a broader audience.
A Hardware Accelerator for Polynomial Multiplication Operation of CRYSTALS-KYBER PQC Scheme

Yaman, Ferhat, Mert, Ahmet Can, Öztürk, Erdinç, and Savaş, Erkay

2021

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Polynomial multiplication is one of the most time-consuming operations utilized in lattice-based post-quantum cryptography (PQC) schemes. CRYSTALS-KYBER is a lattice-based key encapsulation mechanism (KEM) and it was recently announced as one of the four finalists at round three in NIST’s PQC Standardization. Therefore, efficient implementations of polynomial multiplication operation are crucial for high-performance CRYSTALS-KYBER applications. In this paper, we propose three different hardware architectures (lightweight, balanced, high-performance) that implement the NTT, Inverse NTT (INTT) and polynomial multiplication operations for the CRYSTALS-KYBER scheme. The proposed architectures include a unified butterfly structure for optimizing polynomial multiplication and can be utilized for accelerating the key generation, encryption and decryption operations of CRYSTALS-KYBER. Our high-performance hardware with 16 butterfly units shows up to 112×, 132× and 109× improved performance for NTT, INTT and polynomial multiplication, respectively, compared to the high-speed software implementations on Cortex-M4.
ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies

Mağara, Ş. S., Yildirim, C., Yaman, F., Dilekoğlu, B., Tutaş, F. R., Öztürk, E., Kaya, K., Taştan, Ö., and Savaş, E.

2021

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Preserving the privacy and security of big data in the context of cloud computing, while maintaining a certain level of efficiency of its processing remains to be a subject, open for improvement. One of the most popular applications epitomizing said concerns is found to be useful in genome analysis. This work proposes a secure multi-label tumor classification method using homomorphic encryption, whereby two different machine learning algorithms, SVM and XGBoost, are used to classify the encrypted genome data of different tumor types.