English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we study a privacy filter design problem for a sequence ofsensor measurements whose joint probability density function (p.d.f.) dependson a private parameter. To ensure parameter privacy, we propose a filter designframework which consists of two components: a randomizer and a nonlineartransformation. The randomizer takes the private parameter as input andrandomly generates a pseudo parameter. The nonlinear mapping transforms themeasurements such that the joint p.d.f. of the filter's output depends on thepseudo parameter rather than the private parameter. It also ensures that thejoint p.d.f. of the filter's output belongs to the same family of distributionsas that of the measurements. The nonlinear transformation has afeedforward-feedback structure that allows real-time and causal generation ofthe disguised measurements with low complexity using a recursive structure. Thedesign of the randomizer is formulated as an optimization problem subject to aprivacy constraint, in terms of mutual information, and it is shown that theoptimal randomizer is the solution of a convex optimization problem. Usinginformation-theoretic inequalities, we show that the performance of anyestimator of the private parameter, based on the output of the privacy filter,is limited by the privacy constraint. The structure of the nonlineartransformation is studied in the special cases of independent and identicallydistributed, Markovian, and Gauss-Markov measurements. Our results show thatthe privacy filter in the Gauss-Markov case can be implemented as two one-stepahead Kalman predictors and a set of minimum mean square error predictors. TheKalman predictors significantly reduce the complexity of computing thedisguised measurements. A numerical example on occupancy privacy in a buildingautomation system illustrates the approach.

A Model Randomization Approach to Statistical Parameter Privacy