PrivaDE is a privacy-preserving protocol for jointly computing data utility scores in ML using secure computation, with optimizations for efficiency and blockchain integration via smart contracts.
Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption[EB/OL]
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A survey that introduces a unified training pipeline and taxonomizes split learning approaches for LLM fine-tuning across model, system, and privacy dimensions.
The paper synthesizes BCI privacy risks and introduces a three-dimensional framework that grades existing protection methods into four strength levels while flagging mental privacy as an unresolved neuroethical issue.
citing papers explorer
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PrivaDE: Privacy-preserving Data Evaluation for Blockchain-based Data Marketplaces
PrivaDE is a privacy-preserving protocol for jointly computing data utility scores in ML using secure computation, with optimizations for efficiency and blockchain integration via smart contracts.
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A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations
A survey that introduces a unified training pipeline and taxonomizes split learning approaches for LLM fine-tuning across model, system, and privacy dimensions.
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Revisiting Privacy Preservation in Brain-Computer Interfaces: Conceptual Boundaries, Risk Pathways, and a Protection-Strength Grading Framework
The paper synthesizes BCI privacy risks and introduces a three-dimensional framework that grades existing protection methods into four strength levels while flagging mental privacy as an unresolved neuroethical issue.