---
res:
  bibo_abstract:
  - "We study the problem of continually releasing statistics of an evolving dataset
    under differential privacy. In the event-level setting, we show the first polynomial
    lower bounds on the additive error for insertions-only graph problems such as
    maximum matching, degree histogram and k-core number computation. These results
    represent an exponential improvement on the polylogarithmic lower bounds of Fichtenberger,
    Henzinger and Ost [ESA 2021] for the former two problems, and are the first lower
    bounds in the continual release setting for the latter problem. Our results run
    counter to the intuition that the difference between insertions-only vs fully
    dynamic updates causes the gap between polylogarithmic and polynomial additive
    error. Indeed, we show that for estimating the size of the maximum matching or
    k-core number of a vertex, allowing small multiplicative approximations is what
    brings the additive error down to polylogarithmic. We complement these results
    with improved upper bounds on the additive error when no multiplicative approximation
    is allowed.\r\nBeyond graphs, our techniques also show that polynomial additive
    error is unavoidable for the Simultaneous Norm Estimation problem in the insertions-only
    setting. When multiplicative approximations are allowed, we circumvent this lower
    bound by giving the first continual mechanism with polylogarithmic additive error
    under (1 + ζ) multiplicative approximations, for any ζ > 0, for estimating all
    monotone symmetric norms simultaneously.\r\nIn the item-level setting, we show
    polynomial lower bounds on the product of the multiplicative and the additive
    error of continual mechanisms for a large range of graph problems. To the best
    of our knowledge, these are the first lower bounds shown for any differentially
    private mechanism under continual release with multiplicative error. To obtain
    these results, we prove a new lower bound on the product of multiplicative and
    additive error for the 1-Way-Marginals problem, and give reductions from 1-Way-Marginals
    to our desired graph problems. This generalizes the prior results of Hardt and
    Talwar [STOC 2010] and Bun, Ullman and Vadhan [STOC 2014, SIAM J. Comput. 2018],
    who gave lower bounds on the additive error for the special case of mechanisms
    with no multiplicative error.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Bardiya
      foaf_name: Aryanfard, Bardiya
      foaf_surname: Aryanfard
      foaf_workInfoHomepage: http://www.librecat.org/personId=1e8f4084-31df-11ee-b195-f706b4b77091
  - foaf_Person:
      foaf_givenName: Monika H
      foaf_name: Henzinger, Monika H
      foaf_surname: Henzinger
      foaf_workInfoHomepage: http://www.librecat.org/personId=540c9bbd-f2de-11ec-812d-d04a5be85630
    orcid: 0000-0002-5008-6530
  - foaf_Person:
      foaf_givenName: David
      foaf_name: Saulpic, David
      foaf_surname: Saulpic
      foaf_workInfoHomepage: http://www.librecat.org/personId=f8e48cf0-b0ff-11ed-b0e9-b4c35598f964
  - foaf_Person:
      foaf_givenName: A. R.
      foaf_name: Sricharan, A. R.
      foaf_surname: Sricharan
  bibo_doi: 10.1145/3801903
  bibo_issue: '2'
  bibo_volume: 4
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2836-6573
  dct_language: eng
  dct_publisher: Association for Computing Machinery@
  dct_title: Improved lower bounds for privacy under continual release@
...