Highlights
Task:
1. Laplace Mechanism (15 points) Using the same dataset as Homework 1: https://archive.icaucleduhnlidatasets/Aduk. Query the average age of the records with age greater than 25. Inject Laplacian noise to the query result (average age) to ensure 0.5-differential privacy and I-differential privacy.
Tasks:
(a) Derive the global sensitivity for the query (average age). (5 points) (b) Calculate the variance for the Laplace noise and inject noise into the average age query result with 0.5-differential privacy and I-differential privacy, respectively. (10 points)
Submission Part I: (1) a report including the screenshots of the major stem of each task and your answers (named as Itta-report.pdf ), and (2) source code files - all named with the prefix -hw2-1-- (e.g.. hw2-1-laplace.java).
2. Exponential Mechanism (15 points) Using the same dataset as Homework I: https://anthive.ics.uckedniml/datasets/Adult. Query the most frequent -Education" result. Design an exponential mechanism (random-ized) to ensure (-differential privacy for the query. Repeat all the procedures for Exponential mechanism (c = 0.5 and e = I):
Tasks:
(a) Derive the global sensitivity for the query (mast frequent "Education"). (5 points) (b) Compute the probabilities and generate the noisy output result with 0.5-differential privacy and 1-differential privacy, respectively. (10 points) Submission Part II: (1) include the screenshots of the major steps of each task and your answers in the same report as above hw2-report.pdt and (2) source code files - all named with the prefix -hw2-2-' (e.g., hw2-2-exponentiaLjava).
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