What does this equation mean?

where $\mathcal{M}$ is the training mechanism, D and D' are two datasets differing by exactly one record, and S is any set of possible outcomes. In plain terms: the probability of any particular trained model (or any synthetic dataset it produces) coming out of the process is bounded so that it cannot depend too strongly, by a factor set by $\varepsilon$ , on whether any one individual’s record was included or excluded. This is the load-bearing distinction between “privacy-preserving” as a marketing description and as an engineering guarantee: $\varepsilon$ is a number a data controller chooses and can disclose, and a smaller $\varepsilon$ buys a stronger guarantee at the cost of more injected noise and correspondingly lower utility in the resulting synthetic data or model [ 9 ] .