Differential Privacy (DP)
DP is a popular application-level privacy-enabling framework used to protect private or sensitive data on large datasets. This method guarantees an almost identical output when a statistical query is executed on two nearly identical datasets that differ only by the presence or absence of one record.
DP provides security against record linkage attacks by hiding the influence of any single record (for example, individual PII) or records of small groups of users in the predicted outcomes. The process of anonymization and protecting the availability of information related to the presence or absence of individual records in the data-training process is closely associated with the privacy of data against linkage attacks. The cumulative loss is defined as the privacy budget and is called epsilon (ε), which represents the quantifiable amount of privacy provided, where a low value signifies a high level of privacy. The loss is also associated with a decrease...