An innovative online process mining framework for supporting incremental GDPR compliance of business processes

Rashid Zaman; Alfredo Cuzzocrea; Marwan Hassani

doi:10.1109/BigData47090.2019.9005705

An innovative online process mining framework for supporting incremental GDPR compliance of business processes

Rashid Zaman, Alfredo Cuzzocrea, Marwan Hassani

Process Analytics

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

6 Citations (Scopus)

273 Downloads (Pure)

Abstract

GDPR (General Data Protection Regulation) is a new regulation of the European Union that superimposes strict privacy constraints on storing, accessing and processing user data, as a way to ensure that personal user data are not violated neither disclosed without an explicit consent. As a consequence, business processes that interact with large amounts of such data may easily cause GDPR violations, due to the typical complexity of such processes. Inspired by these considerations, this paper highlights the challenges and critical aspects associated with the GDPR compliance journey when opting for naïve straight-forward solutions. We propose a business-aware GDPR compliance journey using online process mining. Using several large log files generated based on a real scenario, we show that the proposed tool is both effective and efficient. As such, it proves to be a powerful concept for usage in incremental GDPR compliance environments.

Original language	English
Title of host publication	2019 IEEE International Conference on Big Data, Big Data 2019
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	2982-2991
Number of pages	10
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9005705
Publication status	Published - Dec 2019
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: 9 Dec 2019 → 12 Dec 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	9/12/19 → 12/12/19

Funding

The first and last authors of the paper have received funding within the BPR4GDPR3 project from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 787149.

Keywords

Business Intelligence
Compliance Checking
General Data Protection Regulation
Model Adaptation
Process Mining

Access to Document

10.1109/BigData47090.2019.9005705

IEEEBigData2019-AuthorVersion

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Zaman, R., Cuzzocrea, A., & Hassani, M. (2019). An innovative online process mining framework for supporting incremental GDPR compliance of business processes. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 2982-2991). Article 9005705 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BigData47090.2019.9005705

Zaman, Rashid ; Cuzzocrea, Alfredo ; Hassani, Marwan. / An innovative online process mining framework for supporting incremental GDPR compliance of business processes. 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Piscataway : Institute of Electrical and Electronics Engineers, 2019. pp. 2982-2991

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title = "An innovative online process mining framework for supporting incremental GDPR compliance of business processes",

abstract = "GDPR (General Data Protection Regulation) is a new regulation of the European Union that superimposes strict privacy constraints on storing, accessing and processing user data, as a way to ensure that personal user data are not violated neither disclosed without an explicit consent. As a consequence, business processes that interact with large amounts of such data may easily cause GDPR violations, due to the typical complexity of such processes. Inspired by these considerations, this paper highlights the challenges and critical aspects associated with the GDPR compliance journey when opting for na{\"i}ve straight-forward solutions. We propose a business-aware GDPR compliance journey using online process mining. Using several large log files generated based on a real scenario, we show that the proposed tool is both effective and efficient. As such, it proves to be a powerful concept for usage in incremental GDPR compliance environments.",

keywords = "Business Intelligence, Compliance Checking, General Data Protection Regulation, Model Adaptation, Process Mining",

author = "Rashid Zaman and Alfredo Cuzzocrea and Marwan Hassani",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9005705",

language = "English",

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booktitle = "2019 IEEE International Conference on Big Data, Big Data 2019",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

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Zaman, R, Cuzzocrea, A & Hassani, M 2019, An innovative online process mining framework for supporting incremental GDPR compliance of business processes. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), 2019 IEEE International Conference on Big Data, Big Data 2019., 9005705, Institute of Electrical and Electronics Engineers, Piscataway, pp. 2982-2991, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 9/12/19. https://doi.org/10.1109/BigData47090.2019.9005705

An innovative online process mining framework for supporting incremental GDPR compliance of business processes. / Zaman, Rashid; Cuzzocrea, Alfredo; Hassani, Marwan.
2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Piscataway: Institute of Electrical and Electronics Engineers, 2019. p. 2982-2991 9005705.

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

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AU - Zaman, Rashid

AU - Cuzzocrea, Alfredo

AU - Hassani, Marwan

PY - 2019/12

Y1 - 2019/12

N2 - GDPR (General Data Protection Regulation) is a new regulation of the European Union that superimposes strict privacy constraints on storing, accessing and processing user data, as a way to ensure that personal user data are not violated neither disclosed without an explicit consent. As a consequence, business processes that interact with large amounts of such data may easily cause GDPR violations, due to the typical complexity of such processes. Inspired by these considerations, this paper highlights the challenges and critical aspects associated with the GDPR compliance journey when opting for naïve straight-forward solutions. We propose a business-aware GDPR compliance journey using online process mining. Using several large log files generated based on a real scenario, we show that the proposed tool is both effective and efficient. As such, it proves to be a powerful concept for usage in incremental GDPR compliance environments.

AB - GDPR (General Data Protection Regulation) is a new regulation of the European Union that superimposes strict privacy constraints on storing, accessing and processing user data, as a way to ensure that personal user data are not violated neither disclosed without an explicit consent. As a consequence, business processes that interact with large amounts of such data may easily cause GDPR violations, due to the typical complexity of such processes. Inspired by these considerations, this paper highlights the challenges and critical aspects associated with the GDPR compliance journey when opting for naïve straight-forward solutions. We propose a business-aware GDPR compliance journey using online process mining. Using several large log files generated based on a real scenario, we show that the proposed tool is both effective and efficient. As such, it proves to be a powerful concept for usage in incremental GDPR compliance environments.

KW - Business Intelligence

KW - Compliance Checking

KW - General Data Protection Regulation

KW - Model Adaptation

KW - Process Mining

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M3 - Conference contribution

SP - 2982

EP - 2991

BT - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

Zaman R, Cuzzocrea A, Hassani M. An innovative online process mining framework for supporting incremental GDPR compliance of business processes. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, 2019 IEEE International Conference on Big Data, Big Data 2019. Piscataway: Institute of Electrical and Electronics Engineers. 2019. p. 2982-2991. 9005705 doi: 10.1109/BigData47090.2019.9005705

An innovative online process mining framework for supporting incremental GDPR compliance of business processes

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