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Fast Optimal Locally Private Mean Estimation via Random Projections

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The study addresses local private mean estimation of high-dimensional vectors, noting sub-optimal error or high complexity in existing solutions. A new framework, ProjUnit, is proposed, which offers computationally efficient algorithms with low communication complexity and near-optimal error by projecting inputs to a random low-dimensional subspace before normalization.

Fast Optimal Locally Private Mean Estimation via Random Projections

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Unlock the Power of AI for Efficient Data Analysis

Are you looking to enhance your company’s data analysis capabilities? Our latest AI solution focuses on the efficient estimation of high-dimensional data. Traditional methods often face challenges such as high error rates and complex communication requirements. Our groundbreaking ProjUnit algorithmic framework overcomes these issues, offering you:

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For personalized advice on AI KPI management, reach out to us at hello@itinai.com. Stay informed with the latest AI insights by following us on Telegram at t.me/itinainews or Twitter at @itinaicom.

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Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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