Conservative or liberal? Personalized differential privacy

Zach Jorgensen, Ting Yu, Graham Cormode

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

49 Citations (Scopus)


Differential privacy is widely accepted as a powerful framework for providing strong, formal privacy guarantees for aggregate data analysis. A limitation of the model is that the same level of privacy protection is afforded for all individuals. However, it is common that the data subjects have quite different expectations regarding the acceptable level of privacy for their data. Consequently, differential privacy may lead to insufficient privacy protection for some users, while over-protecting others. We argue that by accepting that not all users require the same level of privacy, a higher level of utility can often be attained by not providing excess privacy to those who do not want it. We propose a new privacy definition called personalized differential privacy (PDP), a generalization of differential privacy in which users specify a personal privacy requirement for their data. We then introduce several novel mechanisms for achieving PDP. Our primary mechanism is a general one that automatically converts any existing differentially private algorithm into one that satisfies PDP. We also present a more direct approach for achieving PDP, inspired by the well-known exponential mechanism. We demonstrate our framework through extensive experiments on real and synthetic data.

Original languageEnglish
Title of host publicationProceedings - International Conference on Data Engineering
PublisherIEEE Computer Society
Number of pages12
ISBN (Print)9781479979639
Publication statusPublished - 26 May 2015
Event2015 31st IEEE International Conference on Data Engineering, ICDE 2015 - Seoul, Korea, Republic of
Duration: 13 Apr 201517 Apr 2015


Other2015 31st IEEE International Conference on Data Engineering, ICDE 2015
CountryKorea, Republic of


ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Software

Cite this

Jorgensen, Z., Yu, T., & Cormode, G. (2015). Conservative or liberal? Personalized differential privacy. In Proceedings - International Conference on Data Engineering (Vol. 2015-May, pp. 1023-1034). [7113353] IEEE Computer Society.