20 20

Transactions on
Data Privacy
Foundations and Technologies

http://www.tdp.cat


Articles in Press

Accepted articles here

Latest Issues

Year 2024

Volume 17 Issue 1

Year 2023

Volume 16 Issue 3
Volume 16 Issue 2
Volume 16 Issue 1

Year 2022

Volume 15 Issue 3
Volume 15 Issue 2
Volume 15 Issue 1

Year 2021

Volume 14 Issue 3
Volume 14 Issue 2
Volume 14 Issue 1

Year 2020

Volume 13 Issue 3
Volume 13 Issue 2
Volume 13 Issue 1

Year 2019

Volume 12 Issue 3
Volume 12 Issue 2
Volume 12 Issue 1

Year 2018

Volume 11 Issue 3
Volume 11 Issue 2
Volume 11 Issue 1

Year 2017

Volume 10 Issue 3
Volume 10 Issue 2
Volume 10 Issue 1

Year 2016

Volume 9 Issue 3
Volume 9 Issue 2
Volume 9 Issue 1

Year 2015

Volume 8 Issue 3
Volume 8 Issue 2
Volume 8 Issue 1

Year 2014

Volume 7 Issue 3
Volume 7 Issue 2
Volume 7 Issue 1

Year 2013

Volume 6 Issue 3
Volume 6 Issue 2
Volume 6 Issue 1

Year 2012

Volume 5 Issue 3
Volume 5 Issue 2
Volume 5 Issue 1

Year 2011

Volume 4 Issue 3
Volume 4 Issue 2
Volume 4 Issue 1

Year 2010

Volume 3 Issue 3
Volume 3 Issue 2
Volume 3 Issue 1

Year 2009

Volume 2 Issue 3
Volume 2 Issue 2
Volume 2 Issue 1

Year 2008

Volume 1 Issue 3
Volume 1 Issue 2
Volume 1 Issue 1


Volume 7 Issue 3


DPCube: Differentially Private Histogram Release through Multidimensional Partitioning

Yonghui Xiao(a),(*), Li Xiong(a), Liyue Fan(a), Slawomir Goryczka(a), Haoran Li(a)

Transactions on Data Privacy 7:3 (2014) 195 - 222

Abstract, PDF

(a) Department of Mathematics and Computer Science, Emory University, Atlanta, GA, 30322.

e-mail:yxiao25 @emory.edu; lxiong @emory.edu; lfan3 @emory.edu; sgorycz @emory.edu; hli57 @emory.edu


Abstract

Differential privacy is a strong notion for protecting individual privacy in privacy preserving data analysis or publishing. In this paper, we study the problem of differentially private histogram release for random workloads. We study two multidimensional partitioning strategies including: 1) a baseline cell-based partitioning strategy for releasing an equi-width cell histogram, and 2) an innovative 2-phase kd-tree based partitioning strategy for releasing a v-optimal histogram. We formally analyze the utility of the released histograms and quantify the errors for answering linear queries such as counting queries. We formally characterize the property of the input data that will guarantee the optimality of the algorithm. Finally, we implement and experimentally evaluate several applications using the released histograms, including counting queries, classification, and blocking for record linkage and show the benefit of our approach.

* Corresponding author.

Follow us




Supports



ISSN: 1888-5063; ISSN (Digital): 2013-1631; D.L.:B-11873-2008; Web Site: http://www.tdp.cat/
Contact: Transactions on Data Privacy; Vicenç Torra; Umeå University; 90187 Umeå (Sweden); e-mail:tdp@tdp.cat
Note: TDP's web site does not use cookies. TDP does not keep information neither on IP addresses nor browsers. For the privacy policy access here.

 


Vicenç Torra, Last modified: 10 : 31 June 27 2015.