Privacy-Preserving Process Mining in Healthcare

Pika, Anastasiia; Wynn, Moe Thandar; Budiono, Stephanus; Hofstede, Arthur H. M. ter; Aalst, Wil M. P. van der; Reijers, Hajo A.

doi:10.3390/ijerph17051612

Cited by 61 publications

(34 citation statements)

References 20 publications

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“…With a more technological view, a large catalog of mechanisms to protect users' privacy in well-being services have been reported: from protocols to be implemented into cloud infrastructures supporting health services, 13 to solutions to hide personal information in medical records, 14 logs, 15 and other eHealth solutions. 16 Solutions based on traditional cryptographic methods (including revocation procedures and other innovative functionalities) have been reported.…”

Section: Related Workmentioning

confidence: 99%

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Enabling trustworthy personal data protection in eHealth and well-being services through privacy-by-design

Robles

Bordel

Alcarria

et al. 2020

International Journal of Distributed Sensor Networks

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Users are each day more aware of their privacy and data protection. Although this problem is transversal to every digital service, it is especially relevant when critical and personal information is managed, as in eHealth and well-being services. During the last years, many different innovative services in this area have been proposed. However, data management challenges are still in need of a solution. In general, data are directly sent to services but no trustworthy instruments to recover these data or remove them from services are available. In this scheme, services become the users’ data owners although users keep the rights to access, modify, and be forgotten. Nevertheless, the adequate implementation of these rights is not guaranteed, as services use the received data with commercial purposes. In order to address and solve this situation, we propose a new trustworthy personal data protection mechanism for well-being services, based on privacy-by-design technologies. This new mechanism is based on Blockchain networks and indirection functions and tokens. Blockchain networks execute transparent smart contracts, where users’ rights are codified, and store the users’ personal data which are never sent or given to external services. Besides, permissions and privacy restrictions designed by users to be applied to their data and services consuming them are also implemented in these smart contracts. Finally, an experimental validation is also described to evaluate the Quality of Experience (in terms of user satisfaction) and Quality of Service (in terms of processing delay) compared to traditional service provision solutions.

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Section: Related Workmentioning

confidence: 99%

“…In the cloud, service providers perform those operations which are secret as they belong to their business core. As tokens are anonymous parameters representing real data, the result of these initial operations is a parametric service result (equation (15)…”

Section: Trustworthy Data Protection: the Mathematical Approachmentioning

confidence: 99%

Enabling trustworthy personal data protection in eHealth and well-being services through privacy-by-design

Robles

Bordel

Alcarria

et al. 2020

International Journal of Distributed Sensor Networks

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“…In [5], a secure multi-party computation solution is proposed for preserving privacy in an inter-organizational setting. In [13], the authors analyze data privacy and utility requirements for healthcare event data, and the suitability of privacy-preserving techniques is assessed. In [16], privacy metadata in process mining are discussed and a privacy extension for the XES standard (https://xes-standard.org/) is proposed.…”

Section: Related Workmentioning

confidence: 99%

Towards Quantifying Privacy in Process Mining

Rafiei

Aalst

2021

Lecture Notes in Business Information Processing

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Process mining employs event logs to provide insights into the actual processes. Event logs are recorded by information systems and contain valuable information helping organizations to improve their processes. However, these data also include highly sensitive private information which is a major concern when applying process mining. Therefore, privacy preservation in process mining is growing in importance, and new techniques are being introduced. The effectiveness of the proposed privacy preservation techniques needs to be evaluated. It is important to measure both sensitive data protection and data utility preservation. In this paper, we propose an approach to quantify the effectiveness of privacy preservation techniques. We introduce two measures for quantifying disclosure risks to evaluate the sensitive data protection aspect. Moreover, a measure is proposed to quantify data utility preservation for the main process mining activities. The proposed measures have been tested using various real-life event logs.

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“…For instance, privacy is a concern once data is analyzed that is related to people who are not part of the same organization as the one in which the data is analyzed or where the generated insights are used (see Mannhardt et al 2019). This is particularly relevant for mining data from the Internet of Things (Michael et al 2019) and applications in healthcare (Pika et al 2020).…”

Section: Digital Ecosystem Levelmentioning

confidence: 99%