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1. Falcon: Fast Spectral Inference on Encrypted Data

   Qian, Lou; Wen-jie, Lu; Cheng, Hong; Lei, Jiang (October 2020, Advances in neural information processing systems)

   Homomorphic Encryption (HE) based secure Neural Networks(NNs) inference is one of the most promising security solutions to emerging Machine Learning as a Service (MLaaS). In the HE-based MLaaS setting, a client encrypts the sensitive data, and uploads the encrypted data to the server that directly processes the encrypted data without decryption, and returns the encrypted result to the client. The clients' data privacy is preserved since only the client has the private key. Existing HE-enabled Neural Networks (HENNs), however, suffer from heavy computational overheads. The state-of-the-art HENNs adopt ciphertext packing techniques to reduce homomorphic multiplications by packing multiple messages into one single ciphertext. Nevertheless, rotations are required in these HENNs to implement the sum of the elements within the same ciphertext. We observed that HENNs have to pay significant computing overhead on rotations, and each of rotations is ∼10× more expensive than homomorphic multiplications between ciphertext and plaintext. So the massive rotations have become a primary obstacle of efficient HENNs. In this paper, we propose a fast, frequency-domain deep neural network called Falcon, for fast inferences on encrypted data. Falcon includes a fast Homomorphic Discrete Fourier Transform (HDFT) using block-circulant matrices to homomorphically support spectral operations. We also propose several efficient methods to reduce inference latency, including Homomorphic Spectral Convolution and Homomorphic Spectral Fully Connected operations by combing the batched HE and block-circulant matrices. Our experimental results show Falcon achieves the state-of-the-art inference accuracy and reduces the inference latency by 45.45%∼85.34% over prior HENNs on MNIST and CIFAR-10. 

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2. Falcon: Fair and Efficient Online File Transfer Optimization

   https://doi.org/10.1109/TPDS.2023.3282872  

   Arifuzzaman, Md; Bockelman, Brian; Basney, James; Arslan, Engin (January 2023, IEEE Transactions on Parallel and Distributed Systems)
3. FALCON: Framework for Anomaly Detection in Industrial Control Systems

   https://doi.org/10.3390/electronics9081192  

   Sapkota, Subin; Mehdy, A K; Reese, Stephen; Mehrpouyan, Hoda (August 2020, Electronics)

   null (Ed.)

   Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods. 

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4. Hardware Accelerators for Digital Signature Algorithms Dilithium and FALCON

   https://doi.org/10.1109/MDAT.2023.3305156  

   Beckwith, Luke; Nguyen, Duc T.; Gaj, Kris (October 2023, IEEE design test)

   Bhasin, Shivam; Chattopadhyay, Anupam; Güneysu, Tim; Bhunia, Swarup (Ed.)

   Digital signature algorithms are the foundation of many secure communication protocols, including those used in Internet of Things (IoT) applications. While the current generation of signature schemes is secure against classical attacks, they are potentially vulnerable to attacks using quantum computers. Because of this threat, multiple new schemes have been developed and evaluated in recent years. From among these schemes, the National Institute of Standards and Technology standardized two and selected additional three for near-term standardization. For use in IoT, these schemes must be sufficiently efficient in terms of their public-key and signature sizes and the timing of major operations. In this paper, we analyze the choice between two primary schemes considered for extensive use in IoT, CRYSTALS-Dilithium and FALCON, from the point of view of developing efficient hardware accelerators supporting cryptographic operations performed by IoT clients and servers. 

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5. Efficient Hardware RNS Decomposition for Post-Quantum Signature Scheme Falcon

   https://doi.org/10.1109/IEEECONF59524.2023.10476845  

   Coulon, Samuel; He, Pengzhou; Bao, Tianyou; Xie, Jiafeng (October 2023, IEEE)

   The recently announced National Institute of Standards and Technology (NIST) Post-quantum cryptography (PQC) third-round standardization process has released its candidates to be standardized and FALCON is one of them. On the other hand, however, very few hardware implementation works for FALCON have been released due to its very complicated computation procedure and intensive complexity. With this background, in this paper, we propose an efficient hardware structure to implement residue numeral system (RNS) decomposition within NTRUSolve (a key arithmetic component for key generation of FALCON). In total, we have proposed three stages of coherent interdependent efforts to finish the proposed work. First, we have identified the necessary algorithmic operation related to RNS decomposition. Then, we have innovatively designed a hardware structure to realize these algorithms. Finally, field-programmable gate array (FPGA)-based implementation has been carried out to verify the superior performance of the proposed hardware structure. For instance, the proposed hardware design involves at least 3.91x faster operational time than the software implementation. To the authors’ best knowledge, this is the first paper about the hardware acceleration of RNS decomposition for FALCON, and we hope the outcome of this work will facilitate the research in this area. 

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