Robustness of Maximal α-Leakage to Side Information

Jiachun Liao, Lalitha Sankar, Oliver Kosut, Flavio P. Calmon

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

10 Scopus citations


Maximal α-leakage is a tunable measure of information leakage based on the accuracy of guessing an arbitrary function of private data based on public data. The parameter α determines the loss function used to measure the accuracy of a belief, ranging from log-loss at α = 1 to the probability of error at α = ∞. To study the effect of side information on this measure, we introduce and define conditional maximal α-leakage. We show that, for a chosen mapping (channel) from the actual (viewed as private) data to the released (public) data and some side information, the conditional maximal α-leakage is the supremum (over all side information) of the conditional Arimoto channel capacity where the conditioning is on the side information. We prove that if the side information is conditionally independent of the public data given the private data, the side information cannot increase the information leakage.

Original languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781538692912
StatePublished - Jul 2019
Event2019 IEEE International Symposium on Information Theory, ISIT 2019 - Paris, France
Duration: Jul 7 2019Jul 12 2019

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2019 IEEE International Symposium on Information Theory, ISIT 2019

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


Dive into the research topics of 'Robustness of Maximal α-Leakage to Side Information'. Together they form a unique fingerprint.

Cite this