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New algorithm speeds up privacy loss accounting for random allocation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm called multiscale FFT that significantly speeds up privacy loss accounting for random allocation sampling schemes. This method improves upon existing techniques by reducing the computational complexity from quadratic to linear with respect to the accuracy parameter alpha. The new algorithm also introduces a more accurate rounding method, further enhancing privacy bounds for differentially private stochastic gradient descent (DP-SGD). AI

IMPACT Improves privacy guarantees for machine learning training, potentially enabling more sensitive data to be used.

RANK_REASON Academic paper detailing a new algorithm and its performance improvements. [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 algorithm speeds up privacy loss accounting for random allocation

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Academic paper detailing a new algorithm and its performance improvements. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Vitaly Feldman, Moshe Shenfeld ·

    Fast Privacy Loss Accounting for Random Allocation via Multiscale FFT

    arXiv:2610.42117v1 Announce Type: new Abstract: In the random allocation sampling scheme each user's data is used in $k$ steps chosen randomly and uniformly from a sequence of $t$ steps. This scheme is known to improve on Poisson sampling in private optimization and in communicat…

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