Appendix A. More details about differential privacy
As we discussed in chapter 2, differential privacy (DP) is one of the most popular and influential privacy protection schemes. It is based on the concept of making a dataset robust enough that any single substitution in the dataset does not reveal private data. This is typically achieved by calculating the patterns of groups within the dataset, which we call complex statistics, while withholding information about individuals in the dataset. The beauty of differential privacy is its mathematical provability and quantifiability. In the following sections, we will introduce the mathematical foundations and the formal definition of DP. If you are not interested in these mathematical foundations, ...
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