Differentially private data aggregation with optimal utility

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Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning (2017, background): “Several works have adopted DP as an efficient defense mechanism such as [5, 7, 11, 12, 18, 23, 24, 37, 53, 60, 65, 72, 86].”
Advanced Probabilistic Couplings for Differential Privacy (2016, neutral): “t techniques for the veriﬁcation of differential privacy. Using this approach we believe that we are now in a position where we have the tool for formalize “real-world” systems, e.g. as Eigner et al. [25], Shokri and Shmatikov [46] propose. The approach we used here can also be extended to generaliz”
$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy (2016, background): “sualised table widely used in statistic areas that displays multivariate joint distribution of attributes. distributed source. Many different distributed differential privacy techniques were proposed [12]. However, the local noise may be too small to cover the original data. Recently, local privacy ”