Comparing Classifier's Performance under Differential Privacy

Milan Lopuhaä-Zwakenberg*, Mina Sheikhalishahi, Jeroen Kivits, Jordi Klarenbeek, Gert-Jan van der Velde, Nicola Zannone

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
324 Downloads (Pure)


The application of differential privacy in privacy-preserving data analysis has gained momentum in recent years. In particular, it provides an effective solution for the construction of privacy-preserving classifiers, in which one party owns the data and another party is interested in obtaining a classifier model from this data. While several approaches have been proposed in the literature to employ differential privacy for the construction of classifiers, an understanding of the difference in performance of these classifiers is currently missing. This knowledge enables the data owner and the analyst to select the most appropriate classification algorithm and training parameters in order to guarantee high privacy requirements while minimizing the loss of accuracy. In this study, we investigate the impact of the use of differential privacy on three well-known classifiers, i.e., Naïve Bayes, SVM, and Decision Tree classifiers. To this end, we show how these classifiers can be trained i n a differential privacy setting and perform extensive experiments to evaluate the effect of this privacy enforcement on their performance.
Original languageEnglish
Title of host publicationProceedings of the 18th International Conference on Security and Cryptography
EditorsSabrina De Capitani di Vimercati, Pierangela Samarati
Number of pages12
ISBN (Electronic)978-989-758-524-1
Publication statusPublished - Jul 2021
Externally publishedYes
Event18th International Conference on Security and Cryptography, SECRYPT 2021 - Virtual Event
Duration: 6 Jul 20218 Jul 2021
Conference number: 18


Conference18th International Conference on Security and Cryptography, SECRYPT 2021
Abbreviated titleSECRYPT 2021
CityVirtual Event


Dive into the research topics of 'Comparing Classifier's Performance under Differential Privacy'. Together they form a unique fingerprint.

Cite this