Fix 13 : Create SimpleImputer

diffprivlib/SimpleImputer

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+from diffprivlib.tools import mean, to_array
+
+# Assuming your data is stored in a Pandas DataFrame called 'data' and has missing values represented as NaNs
+
+# Step 1: Impute missing values using a differentially private mean imputation
+for column in data.columns:
+    col_values = to_array(data[column].dropna().values)  # Convert column values to array
+    mean_val = mean(col_values, epsilon=1.0)  # Apply differentially private mean imputation
+    data[column] = data[column].fillna(mean_val)  # Fill NaNs with differentially private mean
+
+# Step 2: Add Laplace noise to ensure differential privacy
+# Note: Epsilon and sensitivity should be appropriately chosen based on the context and privacy requirements
+epsilon = 1.0  # Privacy budget
+sensitivity = 1.0  # Sensitivity of the mean operation
+
+# Add Laplace noise to the imputed data
+for column in data.columns:
+    data[column] += np.random.laplace(scale=sensitivity / epsilon, size=len(data))
+
+# Now 'data' contains differentially privately imputed values with added noise
+
+
+#Mean imputation is used to fill missing values for each column in a differentially private manner. 
+# The diffprivlib library provides a mean function that computes the mean in a differentially private way.
+
+# Laplace noise is added to the imputed values to ensure differential privacy. 
+# The scale of the Laplace noise is determined based on the sensitivity of the mean operation and the chosen privacy parameter (epsilon).
+
+# It's crucial to note that the choice of epsilon (privacy parameter) is fundamental to the level of privacy protection. 
+# Lower epsilon values offer higher privacy but might compromise utility. Additionally, determining the sensitivity of operations (such as mean) is crucial for accurate noise addition.
+
+# This approach provides a differentially private imputation strategy by adding noise to the imputed values,
+#  ensuring that individual data points' privacy is protected within a certain privacy budget (controlled by epsilon). 
+# This method maintains the privacy guarantees of differential privacy by carefully considering the privacy parameters used in the operations.%%
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