{ "arguments": { "data": { "type_value": "Array", "description": "Atomic type must be numeric. For Gumbel mechanism, must be limited to a single column of data." }, "candidates": { "type_value": "Array", "default_python": "None", "default_rust": "None", "description": "Set from which the Exponential mechanism will return an element. Type must match with atomic type of data. This value must be column-conformable with data. Only useful for Exponential mechanism." }, "lower": { "type_value": "Array", "default_python": "None", "default_rust": "None", "description": "Estimated minimum possible value of the statistic. Only useful for the snapping mechanism." }, "upper": { "type_value": "Array", "default_python": "None", "default_rust": "None", "description": "Estimated maximum possible value of the statistic. Only useful for the snapping mechanism." } }, "id": "DPMedian", "name": "dp_median", "options": { "mechanism": { "type_proto": "string", "type_rust": "String", "default_python": "\"Automatic\"", "default_rust": "String::from(\"Automatic\")", "description": "Privatizing mechanism to use. Value must be one of [`Exponential`, `Laplace`, `Snapping`, `Gaussian`, `AnalyticGaussian`, `Gumbel`]. `Automatic` chooses `Exponential` if candidates provided, otherwise chooses `Laplace`." }, "privacy_usage": { "type_proto": "repeated PrivacyUsage", "type_rust": "Vec", "default_python": "None", "description": "Object describing the type and amount of privacy to be used for the mechanism release. For Gumbel mechanism, must be limited to a single column of data. Atomic data type value must be float. Example value: {'epsilon': 0.5}" }, "interpolation": { "type_proto": "string", "type_rust": "String", "default_python": "\"midpoint\"", "default_rust": "String::from(\"midpoint\")", "description": "Interpolation strategy. One of [`lower`, `upper`, `midpoint`, `nearest`, `linear`]" } }, "return": { "type_value": "Array", "description": "Differentially private estimates of the median of each column of the data." }, "description": "Returns differentially private estimates of the median of each column of the data.", "proto_id": 13 }