Joseph Geumlek

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Bayesian inference has great promise for the privacy-preserving analysis of sensitive data, as posterior sampling automatically preserves differential privacy, an algorithmic notion of data privacy, under certain conditions (Dimitrakakis et al., 2014; Wang et al., 2015b). While this one posterior sample (OPS) approach elegantly provides privacy “for free,”(More)
With the newly proposed privacy definition of Rényi Differential Privacy (RDP) in [15], we re-examine the inherent privacy of releasing a single sample from a posterior distribution. We exploit the impact of the prior distribution in mitigating the influence of individual data points. In particular, we focus on sampling from an exponential family and(More)
We explore the potential of applying a contextual shape matching algorithm to the domain of video stabilization. This method is a natural fit for finding the point correspondences between subsequent frames in a video. By using global contextual information, this method outperforms methods which only consider local features in cases where the shapes involved(More)
We explore the potential of applying a particular shape matching algorithm[2] to the domain of video stabilization. This method is a natural fit for finding the point correspondences between subsequent frames in a video. By using global contextual information, this method out performs methods which only consider local features in cases where the shapes(More)
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