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New framework uses Shapley values for data privacy and utility

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework that adapts Shapley-value-based feature attribution to address data privacy concerns. This approach captures both disclosure risk and data utility at the feature level, offering a more granular perspective than existing dataset-level methods. The framework is designed to be agnostic to various data masking, statistical, and machine learning techniques, demonstrating effectiveness in reducing disclosure risk while maintaining data utility in experimental results. AI

IMPACT This research could lead to more robust data masking techniques, improving privacy in AI model training and deployment.

RANK_REASON This is a research paper detailing a novel framework for data privacy using Shapley values. [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 framework uses Shapley values for data privacy and utility

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This is a research paper detailing a novel framework for data privacy using Shapley values. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xinxue (Shawn), Qu, Francis Bilson Darku, Hong Guo ·

    Shapley-Value-Based Feature Attribution for Data Masking

    arXiv:2607.28946v1 Announce Type: new Abstract: Despite its many benefits, widespread access to individuals' personal data also causes severe privacy concerns for consumers, companies, and policymakers. This study proposes a novel framework that adapts the Shapley-value-based fea…

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