Protecting privacy when mining and sharing user data

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BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model (2017, background): “For example, [24, 26, 16, 31] address the problem of publishing a subset of the data contained in a search log with differential privacy guarantees; [27] and [6] propose approaches for frequent item identification; [14] propose an approach for monitoring aggregated web browsing activities; and so on”
DP-WHERE: Differentially private modeling of human mobility (2013, unknown): “3) CallsPerDay: WHERE computes an empirical distribution CallsPerDay over the set C = {μ min , . . . μ max } × {σ min , . . . σ max } of possible rounded values of means and standard deviations of numbers of calls per day made by users.”