@inproceedings{a8f0f57a19824c70a98668fb9e50a3c9,
title = "{"}to Tell You the Truth{"} by Interval-Private Data",
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",
author = "Jie Ding and Bangjun Ding",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Big Data, Big Data 2020 ; Conference date: 10-12-2020 Through 13-12-2020",
year = "2020",
month = dec,
day = "10",
doi = "10.1109/BigData50022.2020.9378253",
language = "英语",
series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "25--32",
editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, \{Xiaohua Tony\} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",
booktitle = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
address = "美国",
}