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.
Oblidb: Oblivious query processing for secure databases.arXiv preprint arXiv:1710.00458, 2017
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A lightweight calibration adjusts query optimizer cost models for data movement and RMP translation overheads in CVMs, recovering up to 48% performance on analytical queries.
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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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Query Cost Model Calibration in Confidential Virtual Machines
A lightweight calibration adjusts query optimizer cost models for data movement and RMP translation overheads in CVMs, recovering up to 48% performance on analytical queries.