TLKC-Privacy Model for Process Mining

Rafiei, Majid; Wagner, Miriam; Aalst, Wil M. P. van der

doi:10.1007/978-3-030-50316-1_24

Cited by 24 publications

(38 citation statements)

References 18 publications

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“…In [11], the notion of differential privacy is employed to preserve the privacy of cases. In [17], the TLKC-privacy model is introduced to deal with high variability issues in event logs for applying group-based anonymization techniques. In [5], a secure multi-party computation solution is proposed for preserving privacy in an inter-organizational setting.…”

Section: Related Workmentioning

confidence: 99%

“…Attribute disclosure quantifies how confidently the sensitive attributes of cases (as individuals) can be specified. As discussed in [17], traces play the role of both quasi-identifiers and sensitive attributes. That is, a complete sequence of activities, which belongs to a case, is sensitive person-specific information.…”

Section: Disclosure Riskmentioning

confidence: 99%

“…4.2 for quantifying data utility preservation after applying a privacy preservation technique. We use PPDP-PM [15] as a privacy preservation tool for process mining to apply the TLKC-privacy model [17] to a given event log. The TLKC-privacy model is a group-based privacy preservation technique which provides a good level of flexibility through various parameters such as the type and size (power) of background knowledge.…”

Section: Utility Loss Analysismentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Disclosure Riskmentioning

confidence: 99%

Section: Utility Loss Analysismentioning

confidence: 99%

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Towards Quantifying Privacy in Process Mining

Rafiei

Aalst

2021

Lecture Notes in Business Information Processing

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“…Rafiei et al [43] propose a privacy model called TLKC for publishing event logs, which provides group-based anonymization (M1) based on k-anonymity, and quantifies the risk based on the attacker's background knowledge.…”

Section: Current Workmentioning

confidence: 99%

Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Elkoumy

Fahrenkrog-Petersen

Sani

et al. 2021

ACM Trans. Manage. Inf. Syst.

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Privacy and confidentiality are very important prerequisites for applying process mining to comply with regulations and keep company secrets. This article provides a foundation for future research on privacy-preserving and confidential process mining techniques. Main threats are identified and related to a motivation application scenario in a hospital context as well as to the current body of work on privacy and confidentiality in process mining. A newly developed conceptual model structures the discussion that existing techniques leave room for improvement. This results in a number of important research challenges that should be addressed by future process mining research.

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“…Anonymised View: This provides the whole view of shared datasets. For privacy protection, we applied the Privacy and Anonymity in Information Security (PAIS) algorithm [ 44 ] to achieve the LKC-privacy model [ 45 ]. LKC-privacy prevents record and attribute linkage attacks for high-dimensional datasets.…”

Section: Data Security and Privacymentioning

confidence: 99%

Big Data Warehouse for Healthcare-Sensitive Data Applications

Shahid

Nguyen

Kechadi

2021

Sensors

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Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project “Big Data against Childhood Obesity (BigO)” aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.

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TLKC-Privacy Model for Process Mining

Cited by 24 publications

References 18 publications

Towards Quantifying Privacy in Process Mining

Towards Quantifying Privacy in Process Mining

Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Big Data Warehouse for Healthcare-Sensitive Data Applications

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