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Orion: A Fully Homomorphic Encryption Framework for Deep Learning
TL;DR: Orion is a framework that compiles PyTorch neural network models into efficient CKKS FHE programs for encrypted inference. Orion automatically handles low-level FHE details such as data packing, bootstrap placement, and precision management. Orion is open-sourced at: https://github.com/baahl-nyu/orion.
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DPHE: Protecting Server Privacy in CKKS-based Protocols
TL;DR: We investigate methods for protecting server privacy in CKKS-based protocols. Unlike exact homomorphic encryption schemes, formally defining security notions for the server is challenging in CKKS-based protocols due to the approximate nature of CKKS. We address this by introducing a new security notion called Differentially Private Homomorphic Encryption, which is motivated by differential privacy. Based on this notion, we construct a general compiler that transforms CKKS-based protocols into DPHE protocols. We also present the first zero-knowledge argument of knowledge for CKKS ciphertexts to protect server privacy against malicious clients.
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Homomorphic Encryption for Data Science
TL;DR: We introduce a new high-precision CKKS bootstrapping method. It leverages a novel Integer Cleaning strategy inspired by the Discrete CKKS technique and is implemented using the Grafting technique. We highlight its main building blocks and discuss its efficiency.
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A Novel Asymmetric BSGS Polynomial Evaluation Algorithm under Homomorphic Encryption
TL;DR: We introduce a new polynomial evaluation algorithm under homomorphic encryption, namely the Asymmetric BSGS Algorithm. It is a generalization and specialization of the original Baby-Step Giant-Step algorithm in the leveled FHE computation model. Leveraging the observation that there is a difference in multiplicative depth between the baby-step set and the giant-step set, this algorithm significantly reduces the number of modulus and key switches required for dense polynomial evaluation from $O(\sqrt{d})$ to $O(d^{1/t})$, by adjusting the set decomposition method and relaxing the control of noise growth and ciphertext size in some calculations. Here, $d$ is the polynomial degree and $t$ is a small constant which, according to our experiments, is recommended to be chosen as $4$.
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Leveraging Discrete CKKS to Bootstrap in High Precision
TL;DR: We introduce a new high-precision CKKS bootstrapping method. It leverages a novel Integer Cleaning strategy inspired by the Discrete CKKS technique and is implemented using the Grafting technique. We highlight its main building blocks and discuss its efficiency.