On the difficulty of achieving differential privacy in practice: User-level safeguards in aggregated location data: Although large-scale human mobility data contains crucial information for understanding human behavior, it is also very sensitive.
In the work developed by Bassolas et al., we studied the structure of cities and their impact on urban livability using a highly aggregated mobility dataset. In order to protect privacy, random noise was added using an automated Laplace mechanism (ε, δ)-differential privacy, with ε =0.66 and δ =2.1×10−29. Where ε defines the noise intensity and δ represents the deviation from pure ε privacy. Differential privacy mathematically guarantees that a person, who observes the result of a differential private analysis, is likely to produce the same inference about one's private information or not, that person's private information is combined as input for the analysis
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