src API documentation

# src

This module initializes the Local_Privacy package and specifies the core imports necessary for utilizing its local differential privacy functionalities.

The Local_Privacy package provides a suite of differential privacy algorithms designed to safeguard user data privacy while preserving the utility of aggregated data. These algorithms are organized into distinct modules and subpackages to ensure ease of use, flexibility, and scalability.

Core Modules:

- individual_method.py: Implements the differential privacy algorithm for single-user datasets.
- general_method.py: Implements the differential privacy algorithm for multi-user datasets.

Subpackages:

- private_count_mean: Contains algorithms for performing private mean calculations on count data.
- private_hadamard_count_mean: Implements private mean calculation algorithms utilizing the Hadamard transform.
- rappor: Implements the RAPPOR (Randomized Aggregatable Privacy-Preserving Ordinal Response) algorithm for local differential privacy.
- private_count_min: Contains algorithms for computing private minimum values using the Count-Min-Sketch technique.
- private_count_sketch: Provides algorithms for count sketching with the Count-Sketch method for private data analysis.

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     1"""
     2This module initializes the Local_Privacy package and specifies the core imports
     3necessary for utilizing its local differential privacy functionalities.
     4
     5The Local_Privacy package provides a suite of differential privacy algorithms
     6designed to safeguard user data privacy while preserving the utility of aggregated
     7data. These algorithms are organized into distinct modules and subpackages to ensure
     8ease of use, flexibility, and scalability.
     9
    10Core Modules:
    11- individual_method.py: Implements the differential privacy algorithm for single-user datasets.
    12- general_method.py: Implements the differential privacy algorithm for multi-user datasets.
    13
    14Subpackages:
    15- private_count_mean: Contains algorithms for performing private mean calculations on count data.
    16- private_hadamard_count_mean: Implements private mean calculation algorithms utilizing the Hadamard transform.
    17- rappor: Implements the RAPPOR (Randomized Aggregatable Privacy-Preserving Ordinal Response) algorithm for local differential privacy.
    18- private_count_min: Contains algorithms for computing private minimum values using the Count-Min-Sketch technique.
    19- private_count_sketch: Provides algorithms for count sketching with the Count-Sketch method for private data analysis.
    20"""