Differential privacy using Gamma distribution

Yongbin Park, Minchul Kim, Ji Won Yoon

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The Laplace mechanism is a commonly employed approach that offers privacy guarantees within the framework of differential privacy. Nevertheless, the Laplace mechanism exhibits two limitations. Firstly, the privacy leakage of data can be exacerbated when the general differential private mechanism is accessed repeatedly with the same input owing to the sequential property of differential privacy. Secondly, the Laplace mechanism may not be suitable for some applications that solely involve positive samples as it can yield unwanted negative samples from the Laplace distribution.We address these issues by utilizing the Gamma distribution to handle database entries that must be consist of positive values ranging from 0 to infinity. In our approach, the epsilon parameter of our mechanism is determined by the value with noise according to the definition of differential privacy. Notably, the range of the noise is unbounded on the right thereby epsilon to approach infinity as the value with noise increases. To mitigate this, we impose constraints on the range of the noise in order to reasonably restrict the epsilon value of the mechanism. However, it should be noted that these constraints may impact the probability of ensuring epsilon-differential privacy and necessitate the imposition of a minimum boundary on the values of dataset. Additionally, we propose a new noise parameter that can be used to adjust the probability of ensuring differential privacy for a fixed epsilon.

Original languageEnglish
Title of host publicationProceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781665452458
Publication statusPublished - 2023
Event22nd IEEE Statistical Signal Processing Workshop, SSP 2023 - Hanoi, Viet Nam
Duration: 2023 Jul 22023 Jul 5

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings


Conference22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Country/TerritoryViet Nam

Bibliographical note

Funding Information:
ACKNOWLEDGEMENTS Prof. Yoon was supported by an Institute of Information & Communications Technology Planning Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 021-0-00558-003).

Publisher Copyright:
© 2023 IEEE.


  • different privacy
  • gamma distribution
  • information prevention
  • private mechanism

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


Dive into the research topics of 'Differential privacy using Gamma distribution'. Together they form a unique fingerprint.

Cite this