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Onyx offers cost-efficient, disk-oblivious ANN search for sensitive data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Onyx, a new system for approximate nearest neighbor (ANN) search designed to protect sensitive data when processed on external storage. Onyx addresses the high costs and latency associated with existing oblivious ANN search methods by co-designing its ANN and ORAM components. The system achieves significant improvements, offering 1.7-9.9x lower cost and 2.3-12.3x lower latency compared to prior state-of-the-art solutions. AI

IMPACT Enhances privacy for sensitive data in AI systems, potentially enabling more secure third-party processing.

RANK_REASON Academic paper introducing a novel system for secure data search.

Read on Hugging Face Daily Papers →

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

Onyx offers cost-efficient, disk-oblivious ANN search for sensitive data

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0 / 100
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Research
Academic paper introducing a novel system for secure data search.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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paper, safety, infra
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High
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Story freshness
178 days old
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Onyx: Cost-Efficient Disk-Oblivious ANN Search

    Approximate nearest neighbor (ANN) search in AI systems increasingly handles sensitive data on third-party infrastructure. Trusted execution environments (TEEs) offer protection, but cost-efficient deployments must rely on external SSDs, which leaks user queries through disk acce…

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