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The Bounded Laplace Mechanism in Differential Privacy · Around

2018

The Bounded Laplace Mechanism in Differential Privacy

Holohan, Naoise, Antonatos, Spiros, Braghin, Stefano et al.

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Understand

The Laplace mechanism is the workhorse of differential privacy, applied to many instances where numerical data is processed.

However, the Laplace mechanism can return semantically impossible values, such as negative counts, due to its infinite support.
There are two popular solutions to this: (i) bounding/capping the output values and (ii) bounding the mechanism support.
In this paper, we show that bounding the mechanism support, while using the parameters of the pure Laplace mechanism, does not typically preserve differential privacy.

Built on

Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K.: · 2007
Earlier work this paper cites.
Boosting the accuracy of differentially private histograms through consistency
Hay, M., Rastogi, V., Miklau, G., Suciu, D.: · 2010
Earlier work this paper cites.
Functional mechanism: Regression analysis under differential privacy
Zhang, J., Zhang, Z., Xiao, X., Yang, Y., Winslett, M.: · 2012
Earlier work this paper cites.

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Then

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