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 RAG Framework Uses Homomorphic Encryption for Enhanced Privacy
Researchers have developed "GoldenRetriever," a novel framework for privacy-preserving retrieval-augmented generation (RAG) that utilizes non-interactive homomorphic encryption. This approach addresses the privacy conce…
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New framework automates transformer approximation for faster homomorphic encryption
Researchers have developed ATLAS, an automated framework designed to optimize the approximation of transformer models for efficient homomorphic inference. This new system addresses the challenge of configuring per-layer…
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New pruning method enhances reliability of encrypted neural networks
Researchers have developed a new method called Polynomial-Sensitivity-Aware Pruning (PSAP) to improve the reliability of neural networks when encrypted using homomorphic encryption (HE). PSAP considers weight magnitude,…
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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…