src.private_count_mean API documentation

# src.private_count_mean

This subpackage contains implementations of algorithms for calculating private count means using the Count-Mean-Sketch (CMS) approach. The Count-Mean-Sketch is a probabilistic data structure that allows for efficient frequency estimation while providing differential privacy guarantees. This subpackage includes both client-side and server-side implementations for privacy-preserving data aggregation.

Modules:

- cms_client_mean.py: Implements the client-side logic for generating private count means.
- private_cms_client.py: Contains the client-side logic for perturbing data before sending it to the server.
- private_cms_server.py: Implements the server-side logic for aggregating and analyzing perturbed data.

Main Functions:

- execute_client: Simulates the client side of the privatized Count-Min Sketch for all elements in the dataset.
- server_simulator: Simulates the server side of the privatized Count-Min Sketch, processes the privatized data, and estimates frequencies.
- update_sketch_matrix: Updates the sketch matrix based on the privatized data received from the client.
- estimate_client: Estimates the frequency of an element based on the private CMS sketch matrix.

View Source

     1"""
     2This subpackage contains implementations of algorithms for calculating private count means
     3using the Count-Mean-Sketch (CMS) approach. The Count-Mean-Sketch is a probabilistic data
     4structure that allows for efficient frequency estimation while providing differential privacy
     5guarantees. This subpackage includes both client-side and server-side implementations for
     6privacy-preserving data aggregation.
     7
     8Modules:
     9- cms_client_mean.py: Implements the client-side logic for generating private count means.
    10- private_cms_client.py: Contains the client-side logic for perturbing data before sending it to the server.
    11- private_cms_server.py: Implements the server-side logic for aggregating and analyzing perturbed data.
    12
    13Main Functions:
    14- execute_client: Simulates the client side of the privatized Count-Min Sketch for all elements in the dataset.
    15- server_simulator: Simulates the server side of the privatized Count-Min Sketch, processes the privatized data, and estimates frequencies.
    16- update_sketch_matrix: Updates the sketch matrix based on the privatized data received from the client.
    17- estimate_client: Estimates the frequency of an element based on the private CMS sketch matrix.
    18"""