5 research outputs found
Compressive Privacy for a Linear Dynamical System
We consider a linear dynamical system in which the state vector consists of
both public and private states. One or more sensors make measurements of the
state vector and sends information to a fusion center, which performs the final
state estimation. To achieve an optimal tradeoff between the utility of
estimating the public states and protection of the private states, the
measurements at each time step are linearly compressed into a lower dimensional
space. Under the centralized setting where all measurements are collected by a
single sensor, we propose an optimization problem and an algorithm to find the
best compression matrix. Under the decentralized setting where measurements are
made separately at multiple sensors, each sensor optimizes its own local
compression matrix. We propose methods to separate the overall optimization
problem into multiple sub-problems that can be solved locally at each sensor.
We consider the cases where there is no message exchange between the sensors;
and where each sensor takes turns to transmit messages to the other sensors.
Simulations and empirical experiments demonstrate the efficiency of our
proposed approach in allowing the fusion center to estimate the public states
with good accuracy while preventing it from estimating the private states
accurately
Arbitrarily Strong Utility-Privacy Tradeoff in Multi-Agent Systems
Each agent in a network makes a local observation that is linearly related to
a set of public and private parameters. The agents send their observations to a
fusion center to allow it to estimate the public parameters. To prevent leakage
of the private parameters, each agent first sanitizes its local observation
using a local privacy mechanism before transmitting it to the fusion center. We
investigate the utility-privacy tradeoff in terms of the Cram\'er-Rao lower
bounds for estimating the public and private parameters. We study the class of
privacy mechanisms given by linear compression and noise perturbation, and
derive necessary and sufficient conditions for achieving arbitrarily strong
utility-privacy tradeoff in a multi-agent system for both the cases where prior
information is available and unavailable, respectively. We also provide a
method to find the maximum estimation privacy achievable without compromising
the utility and propose an alternating algorithm to optimize the
utility-privacy tradeoff in the case where arbitrarily strong utility-privacy
tradeoff is not achievable
On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks
In a decentralized Internet of Things (IoT) network, a fusion center receives
information from multiple sensors to infer a public hypothesis of interest. To
prevent the fusion center from abusing the sensor information, each sensor
sanitizes its local observation using a local privacy mapping, which is
designed to achieve both inference privacy of a private hypothesis and data
privacy of the sensor raw observations. Various inference and data privacy
metrics have been proposed in the literature. We introduce the concepts of
privacy implication and non-guarantee to study the relationships between these
privacy metrics. We propose an optimization framework in which both local
differential privacy (data privacy) and information privacy (inference privacy)
metrics are incorporated. In the parametric case where sensor observations'
distributions are known \emph{a priori}, we propose a two-stage local privacy
mapping at each sensor, and show that such an architecture is able to achieve
information privacy and local differential privacy to within the predefined
budgets. For the nonparametric case where sensor distributions are unknown, we
adopt an empirical optimization approach. Simulation and experiment results
demonstrate that our proposed approaches allow the fusion center to accurately
infer the public hypothesis while protecting both inference and data privacy