Doctoral defence: Gamal Elkoumy “Privacy-enhancing technologies for business process mining”

On 12 December at 10:15 Gamal Elkoumy will defend his doctoral thesis “Privacy-enhancing technologies for business process mining” for obtaining the degree of Doctor of Philosophy (in Computer Science).


Prof. Marlon Dumas, University of Tartu

Junior Prof. Han van der Aa, University of Mannheim (Germany)
Assistant Prof. Marwan Hassani, Eindhoven University of Technology (Netherlands)

Process Mining Techniques enable organizations to analyze process execution traces to identify improvement opportunities. Such techniques need the event logs (which record process execution) to be available for data analysts to perform the analysis. These logs contain private information about the individuals for whom a process is being executed. In such cases, organizations need to deploy Privacy-Enhancing Technologies (PETs) to enable the analyst to drive conclusions from the event logs while preserving the privacy of individuals.
While PETs techniques preserve the privacy of individuals inside the organization, they work by perturbing the event logs in such a way that may lead to misleading conclusions of the analysis. They may inject new behaviors into the event logs that are impossible to exist in real-life event logs. For example, some PETs techniques anonymize a hospital event log by injecting a trace that a patient may visit a doctor before checking in inside the hospital.
In this thesis, we propose a set of privacy-preserving approaches that we call Privacy-Preserving Process Mining (PPPM) approaches to strike a balance between the benefits an analyst can get from analyzing these event logs and the requirements imposed on them by privacy regulations (e.g., GDPR). Also, in this thesis, we propose an approach that enables organizations to jointly perform process mining over their data without sharing their private information.
The techniques proposed in this thesis have been proposed as open-source tools. The first tool is Amun, enabling an event log publisher to anonymize their event log before sharing it with an analyst. The second tool is called Libra, which provides an enhanced utility-privacy tradeoff.  The third tool is Shareprom, which enables organizations to construct process maps jointly.

The defence can also be followed in Zoom (Meeting ID: 975 4557 2829, Passcode: ati).

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Doctoral defence: Egils Avots “Brain Abnormality Detection Using Statistical Analysis of Individual Structural Connectivity Networks and EEG Signals”

On December 15 at 12:15 Egils Avots will defend his doctoral thesis “Brain Abnormality Detection Using Statistical Analysis of Individual Structural Connectivity Networks and EEG Signals”