Box 1: Analysis()

The code snippet demonstrates a differentially private aggregation query (calculating the mean) rather than the generation of a synthetic dataset. In the SmartNoise SDK (based on OpenDP), the Analysis() class is the context manager used to define the computation graph, track the state of the privacy budget, and execute the query. QAUILSynthesizer and MWEMSynthesizer are used for synthesizing new, fake datasets that mimic the statistical properties of the original data, which is not the objective here.

Box 2: epsilon

The privacy_usage parameter in the dp_mean function specifies how much privacy budget this specific operation should consume. In differential privacy, the primary budget parameter is epsilon ($\epsilon$), which quantifies the privacy loss. The standard syntax for defining this usage in SmartNoise is a dictionary where the key is 'epsilon' (and optionally 'delta'). The code requires defining the specific budget spend for the mean calculation, making epsilon the correct key.

Microsoft Learn. (2023). Explore differential privacy. "Use the SmartNoise package." Retrieved from https://learn.microsoft.com/en-us/training/modules/explore-differential-privacy/2-smartnoise

Relevance: Confirms the use of with sn.Analysis() as analysis: as the standard context for running DP queries in the Azure ML SmartNoise tutorials.

Microsoft Learn. (2023). Implement differential privacy with the SmartNoise package. "Define the analysis." Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/how-to-differential-privacy

Relevance: Documents the privacy_usage parameter, showing examples of privacy_usage={'epsilon': 1}.

OpenDP/SmartNoise Core Documentation. (n.d.). System Architecture.

Relevance: Describes the Analysis class as the engine for validating and executing the privacy definition.