CSF 2026, 39th IEEE Computer Security Foundations Symposium, 26-29 July 2026, Lisbon, Portugal / Also on Cryptology ePrint Archive, Paper 2026/220
Differential privacy (DP) is one of the most efficient
tools for protecting the privacy of individual data holders under
computation. This property guarantees that the computation
outputs for every pair of adjacent input sets are statistically
indistinguishable with respect to a given parameter ", which
is independent of the likelihood that specific inputs occur or
not. While the distribution of input sets is generally unknown,
in some use cases (approximate) information about it might be
available. If the latter is the case, two adjacent inputs of one
individual are sometimes already obfuscated by other inputs and
the computation itself (i.e., without any additional noise). For
example, if the sum of n independent and identically distributed
uniformly random bits outputs approximately n=2, both values
for the first bit remain (almost) equally likely for large n.
Based on this observation, we present a new DP mechanism
that uses an estimate of the input distribution to reduce the noise
addition (compared to standard DP) and hence improves the
accuracy of the output. We first explore this idea in the central
model, where a single central party collects all data. Then, we
provide a new technique (possibly of independent interest) that
allows multiple entities to jointly generate reduced noise, using
the property of infinite divisibility. This allows each party to
individually add noise to their respective inputs, e.g., in Federated
Analytics applications.
We apply our theoretical results, both for the single and multiparty
setups, to perform data analysis over human resources
data from different subsidiaries within a corporate group. Our
benchmarks show that our new DP mechanism provides more
accurate outputs while retaining the same privacy level as stateof-
the-art DP approaches using the geometric mechanism.
Type:
Conférence
City:
Lisbon
Date:
2026-07-26
Department:
Sécurité numérique
Eurecom Ref:
8668
Copyright:
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