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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. Hardware Acceleration for Fully Homomorphic Encryption Scheme Switching from CKKS to FHEW

   https://doi.org/10.1109/IEEECONF60004.2024.10942749  

   Zhang, Kaiyuan; Wang, Antian; Parhi, Keshab K; Lao, Yingjie (October 2024, IEEE)

   Fully Homomorphic Encryption (FHE) presents a paradigm-shifting framework for performing computations on encrypted data, offering revolutionary implications for privacy-preserving technologies. This paper introduces a novel hardware implementation of scheme switching between two leading FHE schemes targeting different computational needs, i.e., arithmetic HE scheme CKKS, and Boolean HE scheme FHEW. The proposed architecture facilitates dynamic switching between the schemes with improved throughput and latency compared to the software baseline. The proposed architecture computation modules support scheme switching operations involving coefficient conversion, modular switching, and key switching. We also optimize the hardware designs for the pre-processing and post-processing blocks, involving key generation, encryption, and decryption. The effectiveness of our proposed design is verified on the Xilinx U280 Datacenter Acceleration FPGA. We demonstrate that the proposed scheme switching accelerator yields a 365× performance improvement over the software counterpart. 

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3. HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture

   Qian, Lou; Lei, Jiang (July 2021, International Conference on Machine Learning)

   Recently Homomorphic Encryption (HE) is used to implement Privacy-Preserving Neural Networks (PPNNs) that perform inferences directly on encrypted data without decryption. Prior PPNNs adopt mobile network architectures such as SqueezeNet for smaller computing overhead, but we find naïvely using mobile network architectures for a PPNN does not necessarily achieve shorter inference latency. Despite having less parameters, a mobile network architecture typically introduces more layers and increases the HE multiplicative depth of a PPNN, thereby prolonging its inference latency. In this paper, we propose a \textbf{HE}-friendly privacy-preserving \textbf{M}obile neural n\textbf{ET}work architecture, \textbf{HEMET}. Experimental results show that, compared to state-of-the-art (SOTA) PPNNs, HEMET reduces the inference latency by $$59.3%\sim 61.2%$$, and improves the inference accuracy by $$0.4 % \sim 0.5%$$. 

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4. Low-Latency Preprocessing Architecture for Residue Number System via Flexible Barrett Reduction for Homomorphic Encryption

   https://doi.org/10.1109/TCSII.2023.3344604  

   Chiu, Sin-Wei; Parhi, Keshab K. (December 2023, IEEE Transactions on Circuits and Systems II: Express Briefs)

   Data privacy has become a significant concern due to the rapid development of cloud services, Internet of Things, edge devices, and other applications. Homomorphic encryption (HE) addresses the issue by enabling computations to be performed without the decryption of the encrypted message. However, the bottleneck of designing homomorphic encryption hardware is the complexity of computation. To tackle the long integer arithmetic, the residue number system based on the Chinese remainder theorem is used. In this paper, we propose a novel modular reduction architecture that computes the mapping of residual polynomials in parallel with high speed and low latency. We implement our proposed design in the Xilinx Ultrascale+ FPGA board (VCU118). When the input sizes are 360-bit (1440-bit), the frequency is 180MHz (168MHz) with 4 pipelining stages. Also, the area delay product (ADP) of DSP blocks of our design is reduced by 23 and 31 percent, respectively, for 360 and 1440 bits, compared to prior work. 

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5. Ripple: Accelerating Programmable Bootstraps for FHE with Wavelet Approximations

   https://doi.org/10.1007/978-3-031-75757-0_14  

   Gouert, Charles; Ugurbil, Mehmet; Mouris, Dimitris; de_Vega, Miguel; Tsoutsos, Nektarios (October 2024, Springer Nature Switzerland)

   Homomorphic encryption can address key privacy challenges in cloud-based outsourcing by enabling potentially untrusted servers to perform meaningful computation directly on encrypted data. While most homomorphic encryption schemes offer addition and multiplication over ciphertexts natively, any non-linear functions must be implemented as costly polynomial approximations due to this restricted computational model. Nevertheless, the CGGI cryptosystem is capable of performing arbitrary univariate functions over ciphertexts in the form of lookup tables through the use of programmable bootstrapping. While promising, this procedure can quickly become costly when high degrees of precision are required. To address this challenge, we propose Ripple: a framework that introduces different approximation methodologies based on discrete wavelet transforms (DWT) to decrease the number of entries in homomorphic lookup tables while maintaining high accuracy. Our empirical evaluations demonstrate significant error reduction compared to plain quantization methods across multiple non-linear functions. Notably, Ripple improves runtime performance for realistic applications, such as logistic regression and Euclidean distance. 

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