---
res:
  bibo_abstract:
  - This paper is eligible for the Jack Keil Wolf ISIT Student Paper Award. We generalize
    a previous framework for designing utility-optimal differentially private (DP)
    mechanisms via graphs, where datasets are vertices in the graph and edges represent
    dataset neighborhood. The boundary set contains datasets where an individual’s
    response changes the binary-valued query compared to its neighbors. Previous work
    was limited to the homogeneous case where the privacy parameter ε across all datasets
    was the same and the mechanism at boundary datasets was identical. In our work,
    the mechanism can take different distributions at the boundary and the privacy
    parameter ε is a function of neighboring datasets, which recovers an earlier definition
    of personalized DP as special case. The problem is how to extend the mechanism,
    which is only defined at the boundary set, to other datasets in the graph in a
    computationally efficient and utility optimal manner. Using the concept of strongest
    induced DP condition we solve this problem efficiently in polynomial time (in
    the size of the graph).@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Sahel
      foaf_name: Torkamani, Sahel
      foaf_surname: Torkamani
      foaf_workInfoHomepage: http://www.librecat.org/personId=0503e7f8-2d05-11ed-aa17-db0640c720fc
  - foaf_Person:
      foaf_givenName: Javad B.
      foaf_name: Ebrahimi, Javad B.
      foaf_surname: Ebrahimi
  - foaf_Person:
      foaf_givenName: Parastoo
      foaf_name: Sadeghi, Parastoo
      foaf_surname: Sadeghi
  - foaf_Person:
      foaf_givenName: Rafael G.L.
      foaf_name: D'Oliveira, Rafael G.L.
      foaf_surname: D'Oliveira
  - foaf_Person:
      foaf_givenName: Muriel
      foaf_name: Médard, Muriel
      foaf_surname: Médard
  bibo_doi: 10.1109/ISIT50566.2022.9834711
  bibo_volume: 2022
  dct_date: 2022^xs_gYear
  dct_identifier:
  - UT:001254261901131
  dct_isPartOf:
  - http://id.crossref.org/issn/2157-8095
  - http://id.crossref.org/issn/9781665421591
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: Heterogeneous differential privacy via graphs@
...