The Bounded Laplace Mechanism in Differential Privacy
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Abstract (arXiv)
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. We also present a robust method to compute the optimal mechanism parameters to achieve differential privacy in such a setting.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Aug 2018.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 4; 2024: 3; 2025: 1; 2026: 1.
13 papers.
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- 2023Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Modelcited by 4
- 2023Holistic Survey of Privacy and Fairness in Machine Learningcited by 4
- 2022De-Identification of French Unstructured Clinical Notes for Machine Learning Taskscited by 3
- 2021Private and Utility Enhanced Recommendations with Local Differential Privacy and Gaussian Mixture Modelcited by 2
- 2021Learning Numeric Optimal Differentially Private Truncated Additive Mechanismscited by 2
- 2024Privacy Amplification for the Gaussian Mechanism via Bounded Supportcited by 2
- 2023Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networkscited by 1
- 2024Privacy Preserving Reinforcement Learning for Population Processescited by 1
- 2025PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimizationcited by 1
- 2022Distributed Transition Systems with Tags for Privacy Analysiscited by 0
- 2023Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guaranteescited by 0
- 2024Differentially Private Federated Learning without Noise Addition: When is it Possible?cited by 0
1 more
- 2026Lap2: Revisiting Laplace DP-SGD for High Dimensions via Majorization Theory26 Feb 2026
- 2025PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization8 Sep 2025
- 2024Privacy Preserving Reinforcement Learning for Population Processes25 Jun 2024
- 2024Differentially Private Federated Learning without Noise Addition: When is it Possible?6 May 2024
- 2024Privacy Amplification for the Gaussian Mechanism via Bounded Support7 Mar 2024
- 2023Holistic Survey of Privacy and Fairness in Machine Learning28 Jul 2023
- 2023Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees11 Jun 2023
- 2023Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model11 Apr 2023
- 2023Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks17 Mar 2023
- 2022De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks16 Sep 2022
- 2022Distributed Transition Systems with Tags for Privacy Analysis6 Apr 2022
- 2021Learning Numeric Optimal Differentially Private Truncated Additive Mechanisms27 Jul 2021
- 2023Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Modelcited by 4
- 2023Holistic Survey of Privacy and Fairness in Machine Learningcited by 4
- 2022De-Identification of French Unstructured Clinical Notes for Machine Learning Taskscited by 3
- 2021Private and Utility Enhanced Recommendations with Local Differential Privacy and Gaussian Mixture Modelcited by 2
- 2021Learning Numeric Optimal Differentially Private Truncated Additive Mechanismscited by 2
- 2024Privacy Amplification for the Gaussian Mechanism via Bounded Supportcited by 2
- 2023Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networkscited by 1
- 2024Privacy Preserving Reinforcement Learning for Population Processescited by 1
- 2025PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimizationcited by 1
- 2022Distributed Transition Systems with Tags for Privacy Analysiscited by 0
- 2023Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guaranteescited by 0
- 2024Differentially Private Federated Learning without Noise Addition: When is it Possible?cited by 0
12 most cited of 13 All 13 (JSON)
- 2026Lap2: Revisiting Laplace DP-SGD for High Dimensions via Majorization Theory26 Feb 2026
- 2025PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization8 Sep 2025
- 2024Privacy Preserving Reinforcement Learning for Population Processes25 Jun 2024
- 2024Differentially Private Federated Learning without Noise Addition: When is it Possible?6 May 2024
- 2024Privacy Amplification for the Gaussian Mechanism via Bounded Support7 Mar 2024
- 2023Holistic Survey of Privacy and Fairness in Machine Learning28 Jul 2023
- 2023Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees11 Jun 2023
- 2023Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model11 Apr 2023
- 2023Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks17 Mar 2023
- 2022De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks16 Sep 2022
- 2022Distributed Transition Systems with Tags for Privacy Analysis6 Apr 2022
- 2021Learning Numeric Optimal Differentially Private Truncated Additive Mechanisms27 Jul 2021
12 newest of 13 All 13 (JSON)