Glossary

k-Anonymity: This technique makes sure that any piece of data that could identify someone is hidden among at least k-1 other similar pieces. This way, it’s harder to figure out who the data belongs to.

epsilon (Privacy-Accuracy Trade-off): Defines the level of privacy versus accuracy in differential privacy. Lower epsilon increases privacy by adding more noise.

Quasi-Identifying Variables: These are details in your data that, by themselves, might not identify someone, but when combined with other details, could. These are usually altered to help protect privacy.

Membership-Inference Attack (MIA): This type of attack attempts to determine if a particular individual's data was used in a dataset. It involves an attacker who has partial access to the data used to train the model and uses this to try to identify individuals in an anonymized dataset.

Hamming Distance: A measure used to determine the difference between two strings of equal length by counting the number of positions at which the corresponding symbols are different. In privacy testing, it measures how much the original and the anonymized data differ.

F1 Score: A measure used in statistics to assess the accuracy of a test. It considers both the precision (what proportion of positive identifications were actually correct) and the recall (what proportion of actual positives were identified correctly) of the test.

False Discovery Rate (FDR): The proportion of false positives (incorrect positive predictions) among all positive predictions made. A high FDR indicates many false alarms.