675 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
Random projection to preserve patient privacy
With the availability of accessible and widely used cloud services, it is natural that large components of healthcare systems migrate to them; for example, patient databases can be stored and processed in the cloud. Such cloud services provide enhanced flexibility and additional gains, such as availability, ease of data share, and so on. This trend poses serious threats regarding the privacy of the patients and the trust that an individual must put into the healthcare system itself. Thus, there is a strong need of privacy preservation, achieved through a variety of different approaches. In this paper, we study the application of a random projection-based approach to patient data as a means to achieve two goals: (1) provably mask the identity of users under some adversarial-attack settings, (2) preserve enough information to allow for aggregate data analysis and application of machine-learning techniques. As far as we know, such approaches have not been applied and tested on medical data. We analyze the tradeoff between the loss of accuracy on the outcome of machine-learning algorithms and the resilience against an adversary. We show that random projections proved to be strong against known input/output attacks while offering high quality data, as long as the projected space is smaller than the original space, and as long as the amount of leaked data available to the adversary is limited
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