Exam DP-100 topic 3 question 122 discussion

The answer is A - Privacy Test - Determines whether a report adheres to the conditions of differential privacy. - Accuracy Test - Measures whether the reliability of reports falls within the upper and lower bounds given a 95% confidence level. - Utility Test - Determines whether the confidence bounds of a report are close enough to the data while still maximizing privacy. - Bias Test - Measures the distribution of reports for repeated queries to ensure they aren’t unbalanced from here: https://www.virtualidentity.be/what-is-differential-privacy-in-machine-learning-preview/

To measure the distribution of reports for repeated queries to ensure that they are balanced when performing data analysis for differential privacy using the SmartNoise SDK, you should perform a utility test. A utility test measures the quality of the output of a differentially private query and checks whether the output is still useful for the intended purpose. The utility of the output is typically assessed by comparing it to the non-private output and measuring the difference between the two. The goal is to ensure that the difference is not too large, so that the output of the differentially private query is still useful. In this case, measuring the distribution of reports for repeated queries is a way to check the utility of the differentially private query, as it will help you ensure that the output is balanced and not biased towards a particular result. Therefore, the correct answer is D. Utility.

perform a Bias test. This test will help you verify that the differentially private mechanism is not introducing systematic errors in any particular direction, thereby maintaining the integrity and fairness of the analysis.

No doubt! check this https://www.virtualidentity.be/what-is-differential-privacy-in-machine-learning-preview/ https://www.virtualidentity.be/what-is-differential-privacy-in-machine-learning-preview/

A for sure

The privacy loss tester will run the query on the dataset with the specified epsilon value for the specified number of times, and generate a report that shows the distribution of the query results, the privacy loss distribution, and the accuracy metrics.

D To measure the distribution of reports for repeated queries and ensure that they are balanced when performing differential privacy analysis using the SmartNoise SDK, you should perform a Utility test. Utility testing assesses how well the privacy mechanism (in this case, the differential privacy technique) preserves the usefulness or utility of the data. It checks whether the results of repeated queries maintain the desired statistical properties and distribution while adding noise to protect individual privacy. Balancing utility and privacy is a key consideration in differential privacy analysis.

Bias, as explained in the source from avotofu

A bias

Agree with comment from avotofu

A. Bias "Bias: DP algorithms on repeated runs should have a mean signed deviation close to zero and not have a statistically significant deviation greater or lower than zero." https://github.com/opendp/smartnoise-sdk/tree/main/eval/sneval

D=Utility

D is correct