Emergent Mind

Optimal Noise Adding Mechanisms for Approximate Differential Privacy

(1305.1330)
Published May 6, 2013 in cs.DS and cs.CR

Abstract

We study the (nearly) optimal mechanisms in $(\epsilon,\delta)$-approximate differential privacy for integer-valued query functions and vector-valued (histogram-like) query functions under a utility-maximization/cost-minimization framework. We characterize the tradeoff between $\epsilon$ and $\delta$ in utility and privacy analysis for histogram-like query functions ($\ell1$ sensitivity), and show that the $(\epsilon,\delta)$-differential privacy is a framework not much more general than the $(\epsilon,0)$-differential privacy and $(0,\delta)$-differential privacy in the context of $\ell1$ and $\ell2$ cost functions, i.e., minimum expected noise magnitude and noise power. In the same context of $\ell1$ and $\ell2$ cost functions, we show the near-optimality of uniform noise mechanism and discrete Laplacian mechanism in the high privacy regime (as $(\epsilon,\delta) \to (0,0)$). We conclude that in $(\epsilon,\delta)$-differential privacy, the optimal noise magnitude and noise power are $\Theta(\min(\frac{1}{\epsilon},\frac{1}{\delta}))$ and $\Theta(\min(\frac{1}{\epsilon2},\frac{1}{\delta2}))$, respectively, in the high privacy regime.

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