Differential Privacy in Practice
Differential privacy (DP) has attracted considerable attention as the method of choice for releasing aggregate query results making it hard to infer information about individual records in the database. The most common way to achieve DP is to add noise following Laplace distribution. In this paper, we study differential privacy from a utility point of view for single and multiple queries. We examine the relationship between the cumulative probability of noise and the privacy degree. Using this analysis and the notion of relative error, we show when for a given problem it is reasonable to employ a differentially private algorithm without losing a certain level of utility. For the case of multiple queries, we introduce a simple DP method called Differential (DIFF) that adds noise proportional to a query index used to express our preferences for having different noise scales for different queries. We also introduce an equation capturing when DIFF satisfies a user-given relative error threshold.
KeywordsStatistical Databases Differetial Privacy Utility
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