Résumé
Imagine a group of citizens willing to collectively contribute their personal
data for the common good to produce socially useful information, resulting from
data analytics or machine learning computations. Sharing raw personal data with
a centralized server performing the computation could raise concerns about
privacy and a perceived risk of mass surveillance. Instead, citizens may trust
each other and their own devices to engage into a decentralized computation to
collaboratively produce an aggregate data release to be shared. In the context
of secure computing nodes exchanging messages over secure channels at runtime,
a key security issue is to protect against external attackers observing the
traffic, whose dependence on data may reveal personal information. Existing
solutions are designed for the cloud setting, with the goal of hiding all
properties of the underlying dataset, and do not address the specific privacy
and efficiency challenges that arise in the above context. In this paper, we
define a general execution model to control the data-dependence of
communications in user-side decentralized computations, in which differential
privacy guarantees for communication patterns in global execution plans can be
analyzed by combining guarantees obtained on local clusters of nodes. We
propose a set of algorithms which allow to trade-off between privacy, utility
and efficiency. Our formal privacy guarantees leverage and extend recent
results on privacy amplification by shuffling. We illustrate the usefulness of
our proposal on two representative examples of decentralized execution plans
with data-dependent communications.