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CDC
2015
IEEE

Privacy-preserving nonlinear observer design using contraction analysis

3 years 10 months ago
Privacy-preserving nonlinear observer design using contraction analysis
Abstract— Real-time signal processing applications are increasingly focused on analyzing privacy-sensitive data obtained from individuals, and this data might need to be processed through model-based estimators to produce accurate statistics. Moreover, the models used in population dynamics studies, e.g., in epidemiology or sociology, are often necessarily nonlinear. This paper presents a design approach for nonlinear privacypreserving model-based observers, relying on contraction analysis to give differential privacy guarantees to the individuals providing the input data. The approach is illustrated in two applications: estimation of edge formation probabilities in a dynamic social network, and syndromic surveillance relying on an epidemiological model.
Jerome Le Ny
Added 18 Apr 2016
Updated 18 Apr 2016
Type Journal
Year 2015
Where CDC
Authors Jerome Le Ny
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