CKKS
PulseAugur coverage of CKKS — every cluster mentioning CKKS across labs, papers, and developer communities, ranked by signal.
- 2026-05-25 research_milestone Researchers identify and propose a solution for overflow attacks in CKKS-based Fully Homomorphic Encryption for neural networks. source
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New SNLP method boosts FHE Transformer inference efficiency
Researchers have developed a new method called Layer-Parallel Inference (SNLP) to improve the efficiency of Transformer models when performing computations on encrypted data using fully homomorphic encryption (FHE). Tra…
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New Shard method enhances privacy in dense retrieval systems
Researchers have developed a new method called Shard to enhance privacy in dense retrieval systems, which are commonly used for semantic search and retrieval-augmented generation (RAG). Shard addresses the vulnerability…
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New TGHE framework enables privacy-preserving GNN inference on large graphs
Researchers have developed TGHE, a novel framework for privacy-preserving Graph Neural Network (GNN) inference in edge-cloud systems. Unlike previous graph-centric approaches that struggle with large datasets, TGHE util…
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New hybrid method enhances privacy in semantic search
Researchers have developed a novel approach to privacy-aware semantic search that balances data protection with search performance. This method uses Singular Value Decomposition (SVD) to truncate document embeddings int…
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New hybrid method enhances privacy in semantic search systems
Researchers have developed a novel approach to enhance privacy in semantic search systems, which are powered by dense embeddings. The proposed method addresses the risk of embedding-inversion attacks that can reconstruc…
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New FEnc2 Framework Boosts Private AI Inference Efficiency
Researchers have developed FEnc$^2$, a new framework designed to significantly improve the efficiency of private inference using Fully Homomorphic Encryption (FHE). This method unifies data packing by considering both c…
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Overflow vulnerability found in FHE for private neural network inference
Researchers have identified a critical vulnerability in Fully Homomorphic Encryption (FHE) schemes, specifically the widely used CKKS scheme, which can lead to overflow attacks. These attacks corrupt neural network outp…
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New quadratic ReLU replacement speeds up FHE neural network inference
Researchers have developed a new method for replacing the ReLU activation function in neural networks with quadratic polynomials, specifically for use with fully homomorphic encryption (FHE). This approach aims to reduc…