On privacy of socially contagious attributes

Aria Rezaei, Jie Gao

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

2 Scopus citations

Abstract

A common approach to protect user's privacy in data collection is to perform random perturbations on user's sensitive data before collection in a way that aggregated statistics can still be inferred without endangering individual secrets. In this paper, we take a closer look at the validity of Differential Privacy guarantees, when sensitive attributes are subject to social contagion. We first show that in the absence of any knowledge about the contagion network, an adversary that tries to predict the real values from perturbed ones, cannot train a classifier that achieves an area under the ROC curve (AUC) above 1-(1-δ)/(1+e^ϵ), if the dataset is perturbed using an (ϵ,δ)-differentially private mechanism. Then, we show that with the knowledge of the contagion network and model, one can do substantially better. We demonstrate that our method passes the performance limit imposed by differential privacy. Our experiments also reveal that nodes with high influence on others are at more risk of revealing their secrets than others. Our method's superior performance is demonstrated through extensive experiments on synthetic and real-world networks.

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1294-1299
Number of pages6
ISBN (Electronic)9781728146034
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: Nov 8 2019Nov 11 2019

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2019-November
ISSN (Print)1550-4786

Conference

Conference19th IEEE International Conference on Data Mining, ICDM 2019
Country/TerritoryChina
CityBeijing
Period11/8/1911/11/19

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Data Mining
  • Data Privacy
  • Social Computing

Fingerprint

Dive into the research topics of 'On privacy of socially contagious attributes'. Together they form a unique fingerprint.

Cite this