Quantifying Privacy: The Delta-Presence Metric

Delta-Presence Privacy Metric

The delta-presence privacy metric is a measure of the difference between the amount of information that can be inferred about an individual from a dataset before and after the individual's data is removed. It is used to evaluate the privacy of a dataset and to determine the effectiveness of privacy-preserving techniques.

To use the delta-presence privacy metric, first, a dataset is analyzed to determine the amount of information that can be inferred about an individual. Then, the dataset is modified by removing the individual's data, and the analysis is repeated. The difference between the two analyses is the delta-presence privacy metric.

The advantage of the delta-presence privacy metric is that it provides a quantitative measure of the privacy of a dataset. It can be used to compare the privacy of different datasets or to evaluate the effectiveness of different privacy-preserving techniques.

However, the delta-presence privacy metric has some shortcomings. It assumes that the dataset is static and does not change over time, which may not be the case in some situations. Additionally, it does not take into account the possibility of re-identification attacks, where an individual's data can be re-identified even if it has been removed from the dataset.

The delta-presence privacy metric is best used when evaluating the privacy of datasets that contain sensitive information, such as medical records or financial data. It can also be used to evaluate the privacy of datasets used in research studies or for machine learning applications.