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Convergent Evolution: Why Secure Homomorphic Encryption Will Resemble High-Performance GPU Computing
TL;DR: Fully Homomorphic Encryption (FHE) programming hits a fundamental Turing Barrier where secure computation forbids the dynamic branching that makes conventional software work, forcing it into a parallel-first paradigm surprisingly similar to the high-performance GPU model. This means the future of FHE isn't a magic compiler, but a hybrid architecture where a trusted client orchestrates complex logic, while an untrusted server executes simple, branchless secure kernels on encrypted data across a well-defined offloading boundary. Ultimately, developers must stop trying to translate old optimization habits and start redefining problems from the ground up, because in the world of FHE, performance isn't about pruning—it's about parallelism.
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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.
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NeuJeans: Fast Private CNN Inference by Fusing Convolutions and Bootstrapping in FHE
TL;DR: NeuJeans introduces a new “Coefficients-in-Slot” (CinS) encoding for CKKS. It rethinks how convolutions are laid out and fuses them with bootstrapping, cutting latency on big models like ResNet running over ImageNet.
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Ciphertext-Ciphertext Matrix Multiplication: Fast for Large Matrices
TL;DR: We propose fast ciphertext-ciphertext matrix multiplication (CC-MM) algorithms for large matrices. Our algorithms consist of plaintext matrix multiplications (PP-MM) and ciphertext matrix transpose algorithms (C-MT). We introduce and utilize new fast C-MT algorithms for large matrices. By leveraging high-performance BLAS libraries to accelerate PP-MM, we implement large-scale CC-MM with substantial performance improvements.
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Low Communication Threshold Fully Homomorphic Encryption
TL;DR: We propose a solution based on fully homomorphic encryption for privately delegating computation over data from multiple clients to a trusted server. Our construction ensures that every client's data remains private to other participants (server and other clients) even if all but one clients collude against the non-colluding client. It is the first to achieve low communication between all parties, as we also prove that prior low communication solutions to this problem are insecure.