"to Tell You the Truth" by Interval-Private Data

Jie Ding, Bangjun Ding

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

Abstract

We present a new concept of privacy and corresponding mechanisms for privatizing data that will be collected for further learning. The privacy, named as Interval Privacy, enforces the distribution of the raw data conditional on privatized data to be the same as its unconditional distribution over a nontrivial support set. The proposed privatizing mechanism is based on interval censoring techniques, where a set of points is recorded as a set of random intervals containing them. We study some theoretical properties of the proposed privacy mechanism. We demonstrate its use with various examples. Particularly, in the context of supervised regression, we develop a general method that can adapt existing regression algorithms to address interval-valued data.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-32
Number of pages8
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
CountryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Interval Mechanism
  • Interval Privacy
  • Local Privacy
  • Machine Learning
  • Regression

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