Mining Frequent Patterns with Differential Privacy

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Federated Heavy Hitters Discovery with Differential Privacy (2019, methodology): “models of DP (Bhaskar et al., 2010; Bonomi and Xiong, 2013; Diakonikolas et al., 2015; Xu et al., 2016; Zhou and Lin, 2018; Kairouz et al., 2016; Wang et al., 2017; Bassily et al., 2017; Acharya et al., 2018; Ye and Barg, 2018; Avent et al., 2017; Bun et al., 2018; Cormode et al., 2018), and some re”
Privacy for All: Ensuring Fair and Equitable Privacy Protections (2018, methodology): “The value chosen for by different models in the research are 0.01 or 0.1 and in some cases ln2 or ln3 (Bhaskar et al., 2010; Bonomi and Xiong, 2013; Friedman and Schuster, 2010; Li et al., 2012; Zeng et al., 2012).”
The Large Margin Mechanism for Differentially Private Maximization (2014, background): “Subsequent algorithms exploit other properties of itemsets or avoid directly finding the most frequent itemset [34, 27, 15, 8].”