The Bounded Laplace Mechanism in Differential Privacy

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Naoise Holohan
Spiros Antonatos
Stefano Braghin
Pól Mac Aonghusa


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 standard Laplace mechanism, does not typically preserve differential privacy. We also present a robust method to compute the optimal mechanism parameters to achieve differential privacy in such a setting.

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How to Cite
Holohan, Naoise, Spiros Antonatos, Stefano Braghin, and Pól Mac Aonghusa. 2019. “The Bounded Laplace Mechanism in Differential Privacy”. Journal of Privacy and Confidentiality 10 (1).
TPDP 2018