#secure-computation News & Analysis

2 articles tagged with #secure-computation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · May 276/10

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Practical Anonymous Two-Party Gradient Boosting Decision Tree

Researchers introduce an anonymous gradient-boosted decision tree (GBDT) protocol enabling secure training on vertically partitioned data between two parties while hiding record identifiers. The approach uses dual circuit-PSI and oblivious pseudorandom functions to eliminate ID exposure risks inherent in standard private set intersection methods, while achieving computational efficiency comparable to non-private approaches.

AIBullisharXiv – CS AI · Apr 206/10

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Privacy-Preserving LLMs Routing

Researchers propose PPRoute, a privacy-preserving framework for LLM routing that uses Secure Multi-Party Computation (MPC) to protect user data while dynamically selecting between model providers. The system achieves 20x speedup over naive MPC implementations through optimized encoder inference, multi-step model training, and an efficient Top-k algorithm, maintaining routing quality without sacrificing privacy.