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New ML system MammothDP enhances data privacy in TEEs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MammothDP, a novel machine learning system designed to protect sensitive data within trusted execution environments (TEEs). This system is based on the S-BDT gradient boosted decision tree learner and offers provable differential privacy (DP) when used with a privacy filter and timing side-channel hardening. MammothDP introduces an adaptive early stopping criterion for selecting the number of trees without privacy overhead and an improved rollback protection mechanism that is resilient to timing side-channel attacks, aiming for a strong privacy-utility tradeoff. AI

IMPACT Enhances privacy guarantees for machine learning models operating on sensitive data within secure hardware environments.

RANK_REASON This is a research paper detailing a new machine learning system and its technical components. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ML system MammothDP enhances data privacy in TEEs

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This is a research paper detailing a new machine learning system and its technical components. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Moritz Kirschte, Thorsten Peinemann, Kari Kostiainen, Jan Wichelmann, Thomas Eisenbarth, Esfandiar Mohammadi ·

    MammothDP: Private Boosted Decision Trees Ready for Trusted Hardware and Leakage-free Hyperparameters

    arXiv:2611.14317v1 Announce Type: cross Abstract: Data spaces like the European Health Data Space (EHDS) require the protection of sensitive data in studies during the data's processing and the study's release alike. In this work, we investigate how to implement this mandate usin…

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