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差分隐私
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Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
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Graph Structure Learning with Privacy Guarantees for Open Graph Data
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Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning
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Privacy-Utility-Fairness: A Balanced Approach to Vehicular-Traffic Management System
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隐私计算方案:DeepSeek差分隐私实现
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Differential privacy on trust graphs
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面对AI战略的困境,苹果再次打出了隐私牌
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苹果揭秘 Apple 智能开发:差分隐私 + 合成数据,隐私原则贯穿 AI 技术演进
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Build an enterprise synthetic data strategy using Amazon Bedrock
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2025-04-08T16:45:34.000000Z
Interview with Lea Demelius: Researching differential privacy
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Google AI Introduces Parfait: A Privacy-First AI System for Secure Data Aggregation and Analytics
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Unveiling Privacy Risks in Machine Unlearning: Reconstruction Attacks on Deleted Data
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Balancing Privacy and Robustness in NLP: A New Approach for Secure Prompt Learning in LLMs
MarkTechPost@AI
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Privacy Implications and Comparisons of Batch Sampling Methods in Differentially Private Stochastic Gradient Descent (DP-SGD)
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Belt and Braces: When Federated Learning Meets Differential Privacy
Communications of the ACM - Artificial Intelligence
2024-11-26T05:14:50.000000Z
Synthetic Data Outliers: Navigating Identity Disclosure
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2024-11-16T09:05:17.000000Z
DPAdapter: A New Technique Designed to Amplify the Model Performance of Differentially Private Machine Learning DPML Algorithms by Enhancing Parameter Robustness
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Navigating the Challenges of Selective Classification Under Differential Privacy: An Empirical Study
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Synthetic Data Generation in Foundation Models and Differential Privacy: Three Papers from Microsoft Research
Towardsai
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