The paper proves that every non-MDS matrix admits related differentials, every odd-order symmetric MDS matrix admits them, most circulant matrices admit them, and gives an explicit 15-constraint criterion for 3x3 MDS matrices over finite fields.
Homomorphic encryption for arithmetic of approximate numbers
4 Pith papers cite this work, alongside 25 external citations. Polarity classification is still indexing.
4
Pith papers citing it
25
external citations · OpenAlex
representative citing papers
Power-Softmax is a new HE-compatible attention variant that permits training and inference of billion-parameter polynomial LLMs with performance matching standard transformers.
TGHE packs structurally identical ego-graph trees into shared CKKS ciphertexts for parallel encrypted GNN inference, delivering 66.9x speedup on a 3.7M-node financial graph with <0.002 AUC loss.
CPPDD is a new consensus-based protocol for privacy-preserving multi-client data sharing that achieves unanimous-release confidentiality, linear scalability, and high-probability malicious deviation detection.
citing papers explorer
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Analyzing Linear Layers in Related-Differential Cryptanalysis
The paper proves that every non-MDS matrix admits related differentials, every odd-order symmetric MDS matrix admits them, most circulant matrices admit them, and gives an explicit 15-constraint criterion for 3x3 MDS matrices over finite fields.
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Power-Softmax: Towards Secure LLM Inference over Encrypted Data
Power-Softmax is a new HE-compatible attention variant that permits training and inference of billion-parameter polynomial LLMs with performance matching standard transformers.
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TGHE: Template-based Graph Homomorphic Encryption for Privacy-Preserving GNN Inference in Edge-Cloud Systems
TGHE packs structurally identical ego-graph trees into shared CKKS ciphertexts for parallel encrypted GNN inference, delivering 66.9x speedup on a 3.7M-node financial graph with <0.002 AUC loss.
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Secure, Verifiable, and Scalable Multi-Client Data Sharing via Consensus-Based Privacy-Preserving Data Distribution
CPPDD is a new consensus-based protocol for privacy-preserving multi-client data sharing that achieves unanimous-release confidentiality, linear scalability, and high-probability malicious deviation detection.