Study : Privacy Preserving Release of Spatio-temporal Density in Paris

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, methodology): “More precisely, FPA now achieves 0.2 mean PG (vs 0.45 in the previous setting) and EFPAG provides negligible protection against membership inference (compared to 0.3 in Fig.”
Differentially Private Data Generative Models (2018, background): “In practice, differentially private schemes are often tailored to the spatio-temporal location privacy analysis [9, 45, 53, 58, 60].”
Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data (2017, methodology): “Since we target aggregated mobility data, we perform the standard aggregating procedures on the datasets to obtain the ground truth[3, 4, 5].”
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, methodology): “vacy-preserving way, i.e., removing the need for a trusted aggregator [24,36,37]. Moreover, Differential Privacy (DP) [13] can be used to bound the privacy leakage from releasing aggregate statistics [2,21], using output [8,14,39] or input [17,38] perturbation. However, there is no sound method to r”
Private spatial data aggregation in the local setting (2016, background): “Acs and Castelluccia [12] conduct a case study for privately releasing the numbers of individuals in 989 different areas in Paris every hour for an entire week based on CDRs.”