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New framework embeds fingerprints to control healthcare AI data usage

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DataCook, a framework designed to control the usage of healthcare data in AI models. This system embeds "anti-adversarial fingerprints" into data, ensuring that models trained on this "cooked" data perform well on similarly processed inputs but poorly on raw, unauthorized data. Initial evaluations across various medical imaging and tabular datasets show that DataCook can maintain authorized model utility while hindering unauthorized use, demonstrating a potential method for deployment-stage data usage control. AI

IMPACT This framework could offer a novel approach to protecting sensitive healthcare data from unauthorized use in AI model training.

RANK_REASON The cluster contains a research paper detailing a new framework for data usage control in AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New framework embeds fingerprints to control healthcare AI data usage

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The cluster contains a research paper detailing a new framework for data usage control in AI. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sihan Shang, Jun Yan, Danli Shi, Haizhong Zheng, Shuangjia Zheng, Wei Zhao, Jiancheng Yang ·

    DataCook: Exploring Anti-Adversarial Fingerprints for Healthcare Data Usage Control

    arXiv:2403.17755v2 Announce Type: replace Abstract: Purpose: Healthcare AI increasingly relies on high-quality datasets, yet medical data are scarce, costly to curate, and tightly regulated in terms of data access. More critically, once medical data are leaked or accessed without…

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