[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1109/BigDataCongress.2015.25guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

Sensitive Disclosures under Differential Privacy Guarantees

Published: 27 June 2015 Publication History

Abstract

Non-independent reasoning (NIR) refers to learning the information of one record from other records, under the assumption that these records share the same underlying distribution. Accurate NIR could disclose private information of an individual. An important assumption made by differential privacy is that NIR is considered to be non-violation of privacy. In this work, we investigate the extent to which private information of an individual may be disclosed through NIR by query answers that satisfy differential privacy. We first define what a disclosure means under NIR by randomized query answers. We then present a formal analysis on such disclosures by differentially private query answers. Our analysis on real life datasets demonstrates that while disclosures of NIR can be eliminated by adopting a more restricted setting of differential privacy, such settings adversely affects the utility of query answers for data analysis, and this conflict can not be easily solved because both disclosures and utility depend on the accuracy of noisy query answers. This study suggests that under the assumption that the disclosure through NIR is a privacy concern, differential privacy is not suitable because it does not provide both privacy and utility.

Cited By

View all

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings
BIGDATACONGRESS '15: Proceedings of the 2015 IEEE International Congress on Big Data
June 2015
799 pages
ISBN:9781467372787

Publisher

IEEE Computer Society

United States

Publication History

Published: 27 June 2015

Author Tags

  1. Data Privacy
  2. Differential Privacy

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 21 Dec 2024

Other Metrics

Citations

Cited By

View all

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media