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

Jie Ding; Bangjun Ding

doi:10.1109/BigData50022.2020.9378253

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

Jie Ding, Bangjun Ding

Statistics (Twin Cities)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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 language	English (US)
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Editors	Xintao 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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	25-32
Number of pages	8
ISBN (Electronic)	9781728162515
DOIs	https://doi.org/10.1109/BigData50022.2020.9378253
State	Published - Dec 10 2020
Event	8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference	8th IEEE International Conference on Big Data, Big Data 2020
Country/Territory	United States
City	Virtual, Atlanta
Period	12/10/20 → 12/13/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Interval Mechanism
Interval Privacy
Local Privacy
Machine Learning
Regression

Publisher link

10.1109/BigData50022.2020.9378253

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Ding, J., & Ding, B. (2020). "to Tell You the Truth" by Interval-Private Data. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 25-32). Article 9378253 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData50022.2020.9378253

"to Tell You the Truth" by Interval-Private Data. / Ding, Jie; Ding, Bangjun.
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. ed. / Xintao 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. Institute of Electrical and Electronics Engineers Inc., 2020. p. 25-32 9378253 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ding, J & Ding, B 2020, "to Tell You the Truth" by Interval-Private Data. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020., 9378253, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, Institute of Electrical and Electronics Engineers Inc., pp. 25-32, 8th IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, United States, 12/10/20. https://doi.org/10.1109/BigData50022.2020.9378253

Ding J, Ding B. "to Tell You the Truth" by Interval-Private Data. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 25-32. 9378253. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9378253

Ding, Jie ; Ding, Bangjun. / "to Tell You the Truth" by Interval-Private Data. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. editor / Xintao 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. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 25-32 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

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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.",

keywords = "Interval Mechanism, Interval Privacy, Local Privacy, Machine Learning, Regression",

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A2 - Wu, Xintao

A2 - Jermaine, Chris

A2 - Xiong, Li

A2 - Hu, Xiaohua Tony

A2 - Kotevska, Olivera

A2 - Lu, Siyuan

A2 - Xu, Weijia

A2 - Aluru, Srinivas

A2 - Zhai, Chengxiang

A2 - Al-Masri, Eyhab

A2 - Chen, Zhiyuan

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

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

Abstract

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Bibliographical note

Keywords

Publisher link

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