TENNOR enables efficient private training of wide neural networks in TEEs by recasting sparsification as doubly oblivious LSH retrievals and introducing MP-WTA to cut hash table memory by 50x while preserving accuracy.
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Styx integrates sticky policies with TEEs to enforce data-specific rules throughout the full lifecycle in multi-party collaborative computing.
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TENNOR: Trustworthy Execution for Neural Networks through Obliviousness and Retrievals
TENNOR enables efficient private training of wide neural networks in TEEs by recasting sparsification as doubly oblivious LSH retrievals and introducing MP-WTA to cut hash table memory by 50x while preserving accuracy.
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Styx: Collaborative and Private Data Processing With TEE-Enforced Sticky Policy
Styx integrates sticky policies with TEEs to enforce data-specific rules throughout the full lifecycle in multi-party collaborative computing.