Secure, Privacy-Preserving Analysis of Distributed Databases

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Privacy-Preserving Analysis of Vertically Partitioned Data Using Secure Matrix Products (2009, methodology): “Underlying these methods are quantified measures of data utility and disclosure risk (for individual records) (Karr et al. 2006a).”
PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers (2016, background): “Extensive efforts have focused on protecting privacy in logistic regression, from centralized solutions [El Emam et al., 2013] to distributed architecture [Karr et al., 2007, Wolfson et al., 2010, Nardi et al., 2012, Li et al., 2015, Wu et al., 2012b,a].”
Data Safe Havens in health research and healthcare (2015, background): “…difficult to obtain in practice or counterproductive from a scientific perspective (Hansson, 2010), and/or in any setting where the public and patients understand the reasons for, and are supportive of, ‘research without consent’ because it brings direct public benefit (Lecouturier et al., 2008).”
Secure function evaluation based on secret sharing and homomorphic encryption (2009, methodology): “If, as explained above, we consider the star network as merely facilitating the construction of arbitrary rings of data centers, then this method of computing sums of powers becomes similar to the scheme described in [12].”