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Differential privacy is a property of an algorithm (or if you like, a probability distribution over functions), not a property of either the inputs or the outputs of that algorithm. The definition of differential privacy has a universal quantifier over its inputs: for all pairs of inputs that differ in at most one record, the probabilities of any outcome ...


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It would be pretty useful if you would describe particularly what you're doing. This is because it can be easy to use more context to design more efficient DP algorithms. Consider a database $D$ where each user has some number in $[k]$ associated with them, and you want to return a differentially private estimate of: $$p_k = \Pr_{x\in D}[x = k]$$ You're ...


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No, it means that the functions are chosen from some domain with some probability distribution. This is standard for randomized algorithms. For simplicity, assume there are $N$ randomized functions $\mathcal{K}$ possible, and one choose one uniformly with probability $1/N.$ For example, if we restricted ourselves to polynomials of degree $\leq k$ over $...


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