fully homomorphic encryption
PulseAugur coverage of fully homomorphic encryption — every cluster mentioning fully homomorphic encryption across labs, papers, and developer communities, ranked by signal.
9 day(s) with sentiment data
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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 research explores privacy techniques for computer vision systems
Two new research papers explore methods for enhancing privacy in computer vision systems. The first paper, "PrivacyBench," introduces a framework to evaluate combinations of privacy techniques, revealing that combining …
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Chengyi Technology unveils fully homomorphic encryption database for AI data security
Chengyi Technology, represented by co-founder Yu Gongshan, is developing a fully homomorphic encryption (FHE) based database product designed to address data security challenges in the AI era. This technology aims to en…
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New ComputeFHE library simplifies privacy-preserving applications using FHE
A new open-source C++ library called ComputeFHE has been developed to simplify the creation of privacy-preserving applications using Fully Homomorphic Encryption (FHE). The library, built on OpenFHE and the TFHE cryptos…
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New tool HERTA finds 21 bugs in privacy-preserving encryption frameworks
Researchers have developed HERTA, a novel automated testing tool designed to identify vulnerabilities within fully homomorphic encryption (FHE) frameworks. These frameworks, crucial for privacy-preserving computations i…
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New tool HERTA finds 21 bugs in fully homomorphic encryption frameworks
Researchers have developed HERTA, a novel automated testing tool designed to identify vulnerabilities within fully homomorphic encryption (FHE) frameworks. FHE enables computations on encrypted data, crucial for privacy…
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Cerebras highlights privacy risks in LLM processing, proposing FHE as a solution
Cerebras has highlighted a significant privacy concern in current AI systems, where LLM queries are decrypted and processed in plain text on servers, exposing sensitive data to the model. The company suggests that fully…
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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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Post-Quantum DeFi Framework Enhances Financial Inclusivity
A new research paper proposes a post-quantum secure federated DeFi framework to enhance financial inclusivity for underserved individuals. The system uses lattice-based Fully Homomorphic Encryption (FHE) to allow multip…
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Amazon SageMaker enables encrypted ML inference with FHE
Amazon SageMaker is now supporting end-to-end encrypted machine learning inference using fully homomorphic encryption (FHE). This advancement allows for secure processing of sensitive data without decryption, enhancing …
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AWS SageMaker enables encrypted ML inference with FHE
Amazon SageMaker AI now supports end-to-end encrypted machine learning inference using fully homomorphic encryption (FHE). This allows sensitive data, such as medical records or proprietary business information, to be p…
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New research advances differential privacy in machine learning
Researchers have developed new methods to enhance differential privacy in machine learning, particularly for decentralized and causal structure learning. One approach, DPDL, uses a similarity-based calibration technique…
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ML training under FHE gets convergence guarantees and privacy
Researchers have developed a new method for training machine learning models using fully homomorphic encryption (FHE), which allows computations on encrypted data without decryption. This approach provides the first the…
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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…
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Tinfoil launches cloud AI service with verifiable privacy using secure enclaves
Tinfoil, a startup founded by researchers from MIT and Cloudflare, has launched a new service designed to provide verifiable privacy for AI workloads hosted in the cloud. The platform utilizes secure enclave technology,…