• DocumentCode
    1665240
  • Title

    Sensitive Disclosures under Differential Privacy Guarantees

  • Author

    Chao Han ; Ke Wang

  • Author_Institution
    Simon Fraser Univ., Burnaby, BC, Canada
  • fYear
    2015
  • Firstpage
    110
  • Lastpage
    117
  • 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.
  • Keywords
    data privacy; inference mechanisms; learning (artificial intelligence); query processing; security of data; NIR disclosure; data analysis; differential privacy guarantee; differentially private query answers; formal analysis; learning; nonindependent reasoning; privacy nonviolation; private information disclosure; randomized query answers; sensitive disclosure; Accuracy; Data analysis; Data privacy; Noise; Noise measurement; Privacy; Sensitivity; Data Privacy; Differential Privacy;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2015 IEEE International Congress on
  • Conference_Location
    New York, NY
  • Print_ISBN
    978-1-4673-7277-0
  • Type

    conf

  • DOI
    10.1109/BigDataCongress.2015.25
  • Filename
    7207209