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Research PaperResearchia:202602.18007

Natural Privacy Filters Are Not Always Free: A Characterization of Free Natural Filters

Matthew Regehr

Abstract

We study natural privacy filters, which enable the exact composition of differentially private (DP) mechanisms with adaptively chosen privacy characteristics. Earlier privacy filters consider only simple privacy parameters such as Rényi-DP or Gaussian DP parameters. Natural filters account for the entire privacy profile of every query, promising greater utility for a given privacy budget. We show that, contrary to other forms of DP, natural privacy filters are not free in general. Indeed, we sho...

Submitted: February 18, 2026Subjects: Cybersecurity; Computer Science

Description / Details

We study natural privacy filters, which enable the exact composition of differentially private (DP) mechanisms with adaptively chosen privacy characteristics. Earlier privacy filters consider only simple privacy parameters such as Rényi-DP or Gaussian DP parameters. Natural filters account for the entire privacy profile of every query, promising greater utility for a given privacy budget. We show that, contrary to other forms of DP, natural privacy filters are not free in general. Indeed, we show that only families of privacy mechanisms that are well-ordered when composed admit free natural privacy filters.


Source: arXiv:2602.15815v1 - http://arxiv.org/abs/2602.15815v1 PDF: https://arxiv.org/pdf/2602.15815v1 Original Link: http://arxiv.org/abs/2602.15815v1

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Date:
Feb 18, 2026
Topic:
Computer Science
Area:
Cybersecurity
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