Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions

Alfredo Cuzzocrea; Ernesto Damiani

doi:10.1109/BigData52589.2021.9671686

Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions

Alfredo Cuzzocrea
, Ernesto Damiani

Electrical Engineering

Università della Calabria
LORIA Laboratoire Lorrain de Recherche en Informatique et ses Applications

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

14 Scopus citations

Abstract

Big data exchange is an emerging problem in the context of big data management and analytics. In big data exchange, multiple entities exchange big datasets beyond the common data integration or data sharing paradigms, mostly in the context of data federation architectures. How to make big data exchange while ensuring privacy preservation constraints The latter is a critical research challenge that is gaining momentum on the research community, especially due to the wide family of application scenarios where it plays a critical role (e.g., social networks, bio-informatics tools, smart cities systems and applications, and so forth). Inspired by these considerations, in this paper we provide an overview of models and issues in the context of privacy-preserving big data exchange research, along with a selection of future research directions that will play a critical role in next-generation research.

Original language	British English
Title of host publication	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
Editors	Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5081-5084
Number of pages	4
ISBN (Electronic)	9781665439022
DOIs	https://doi.org/10.1109/BigData52589.2021.9671686
State	Published - 2021
Event	2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: 15 Dec 2021 → 18 Dec 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference	2021 IEEE International Conference on Big Data, Big Data 2021
Country/Territory	United States
City	Virtual, Online
Period	15/12/21 → 18/12/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Advanced Tools and Systems for Big Data Processing
Big Data Exchange
Privacy-Preserving Big Data Exchange
Theoretical Problems in Big Data

Access to Document

10.1109/BigData52589.2021.9671686

OpenUrl availability

Full text

Cite this

Cuzzocrea, A., & Damiani, E. (2021). Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions. In Y. Chen, H. Ludwig, Y. Tu, U. Fayyad, X. Zhu, X. T. Hu, S. Byna, X. Liu, J. Zhang, S. Pan, V. Papalexakis, J. Wang, A. Cuzzocrea, & C. Ordonez (Eds.), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 (pp. 5081-5084). (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData52589.2021.9671686

Cuzzocrea, Alfredo ; Damiani, Ernesto. / Privacy-Preserving Big Data Exchange : Models, Issues, Future Research Directions. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. editor / Yixin Chen ; Heiko Ludwig ; Yicheng Tu ; Usama Fayyad ; Xingquan Zhu ; Xiaohua Tony Hu ; Suren Byna ; Xiong Liu ; Jianping Zhang ; Shirui Pan ; Vagelis Papalexakis ; Jianwu Wang ; Alfredo Cuzzocrea ; Carlos Ordonez. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 5081-5084 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

@inproceedings{89b5a6bf014540a7bbac7388997687dc,

title = "Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions",

abstract = "Big data exchange is an emerging problem in the context of big data management and analytics. In big data exchange, multiple entities exchange big datasets beyond the common data integration or data sharing paradigms, mostly in the context of data federation architectures. How to make big data exchange while ensuring privacy preservation constraints The latter is a critical research challenge that is gaining momentum on the research community, especially due to the wide family of application scenarios where it plays a critical role (e.g., social networks, bio-informatics tools, smart cities systems and applications, and so forth). Inspired by these considerations, in this paper we provide an overview of models and issues in the context of privacy-preserving big data exchange research, along with a selection of future research directions that will play a critical role in next-generation research.",

keywords = "Advanced Tools and Systems for Big Data Processing, Big Data Exchange, Privacy-Preserving Big Data Exchange, Theoretical Problems in Big Data",

author = "Alfredo Cuzzocrea and Ernesto Damiani",

note = "Funding Information: ACKNOWLEDGMENTS This research has been partially supported by the French PIA project “Lorraine Universit{\'e} d{\textquoteright}Excellence”, reference ANR-15-IDEX-04-LUE. Funding Information: This research has been partially supported by the French PIA project Lorraine Universite d'Excellence, reference ANR-15-IDEX-04-LUE. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",

year = "2021",

doi = "10.1109/BigData52589.2021.9671686",

language = "British English",

series = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5081--5084",

editor = "Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama Fayyad and Xingquan Zhu and Hu, \{Xiaohua Tony\} and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez",

booktitle = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",

address = "United States",

}

Cuzzocrea, A & Damiani, E 2021, Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions. in Y Chen, H Ludwig, Y Tu, U Fayyad, X Zhu, XT Hu, S Byna, X Liu, J Zhang, S Pan, V Papalexakis, J Wang, A Cuzzocrea & C Ordonez (eds), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, Institute of Electrical and Electronics Engineers Inc., pp. 5081-5084, 2021 IEEE International Conference on Big Data, Big Data 2021, Virtual, Online, United States, 15/12/21. https://doi.org/10.1109/BigData52589.2021.9671686

Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions. / Cuzzocrea, Alfredo; Damiani, Ernesto.
Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. ed. / Yixin Chen; Heiko Ludwig; Yicheng Tu; Usama Fayyad; Xingquan Zhu; Xiaohua Tony Hu; Suren Byna; Xiong Liu; Jianping Zhang; Shirui Pan; Vagelis Papalexakis; Jianwu Wang; Alfredo Cuzzocrea; Carlos Ordonez. Institute of Electrical and Electronics Engineers Inc., 2021. p. 5081-5084 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

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

TY - GEN

T1 - Privacy-Preserving Big Data Exchange

T2 - 2021 IEEE International Conference on Big Data, Big Data 2021

AU - Cuzzocrea, Alfredo

AU - Damiani, Ernesto

N1 - Funding Information: ACKNOWLEDGMENTS This research has been partially supported by the French PIA project “Lorraine Université d’Excellence”, reference ANR-15-IDEX-04-LUE. Funding Information: This research has been partially supported by the French PIA project Lorraine Universite d'Excellence, reference ANR-15-IDEX-04-LUE. Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - Big data exchange is an emerging problem in the context of big data management and analytics. In big data exchange, multiple entities exchange big datasets beyond the common data integration or data sharing paradigms, mostly in the context of data federation architectures. How to make big data exchange while ensuring privacy preservation constraints The latter is a critical research challenge that is gaining momentum on the research community, especially due to the wide family of application scenarios where it plays a critical role (e.g., social networks, bio-informatics tools, smart cities systems and applications, and so forth). Inspired by these considerations, in this paper we provide an overview of models and issues in the context of privacy-preserving big data exchange research, along with a selection of future research directions that will play a critical role in next-generation research.

AB - Big data exchange is an emerging problem in the context of big data management and analytics. In big data exchange, multiple entities exchange big datasets beyond the common data integration or data sharing paradigms, mostly in the context of data federation architectures. How to make big data exchange while ensuring privacy preservation constraints The latter is a critical research challenge that is gaining momentum on the research community, especially due to the wide family of application scenarios where it plays a critical role (e.g., social networks, bio-informatics tools, smart cities systems and applications, and so forth). Inspired by these considerations, in this paper we provide an overview of models and issues in the context of privacy-preserving big data exchange research, along with a selection of future research directions that will play a critical role in next-generation research.

KW - Advanced Tools and Systems for Big Data Processing

KW - Big Data Exchange

KW - Privacy-Preserving Big Data Exchange

KW - Theoretical Problems in Big Data

UR - https://www.scopus.com/pages/publications/85125340703

U2 - 10.1109/BigData52589.2021.9671686

DO - 10.1109/BigData52589.2021.9671686

M3 - Conference contribution

AN - SCOPUS:85125340703

T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

SP - 5081

EP - 5084

BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

A2 - Chen, Yixin

A2 - Ludwig, Heiko

A2 - Tu, Yicheng

A2 - Fayyad, Usama

A2 - Zhu, Xingquan

A2 - Hu, Xiaohua Tony

A2 - Byna, Suren

A2 - Liu, Xiong

A2 - Zhang, Jianping

A2 - Pan, Shirui

A2 - Papalexakis, Vagelis

A2 - Wang, Jianwu

A2 - Cuzzocrea, Alfredo

A2 - Ordonez, Carlos

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 15 December 2021 through 18 December 2021

ER -

Cuzzocrea A, Damiani E. Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions. In Chen Y, Ludwig H, Tu Y, Fayyad U, Zhu X, Hu XT, Byna S, Liu X, Zhang J, Pan S, Papalexakis V, Wang J, Cuzzocrea A, Ordonez C, editors, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 5081-5084. (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). doi: 10.1109/BigData52589.2021.9671686

Privacy-Preserving Big Data Exchange: Models, Issues, Future Research Directions

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

OpenUrl availability

Other files and links

Fingerprint

Cite this