Quantifying the Bit-Error Resilience of FHE Compute
Augusto Vega, Matías Mazzanti, et al.
HPCA 2025
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.
Augusto Vega, Matías Mazzanti, et al.
HPCA 2025
Frederico Araujo, Teryl Taylor
ESEC/FSE 2020
Darya Kaviani, Sijun Tan, et al.
OSDI 2024
Daniel Egger, Jakub Marecek, et al.
APS March Meeting 2021