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Published equation contexts

D′D'

Why this formula appears here

where M\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 ε\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: ε\varepsilon is a number a data controller chooses and can disclose, and a smaller ε\varepsilon buys a stronger guarantee at the cost of more injected noise…

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DD

Symbol D

D is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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Published contexts (1)

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D′D'

Equation 12 · AI Research

How Training Data and Synthetic Data Actually Work

This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.

where M\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 ε\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: ε\varepsilon is a number a data controller chooses and can disclose, and a smaller ε\varepsilon buys a stronger guarantee at the cost of more injected noise…

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