Mining Encrypted Software Logs using Alpha Algorithm

Tillem, Gamze; Erkin, Zekeriya; Lagendijk, Reginald L.

doi:10.5220/0006408602670274

Cited by 8 publications

(5 citation statements)

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“…Rafiee et al proposed a confidentiality framework and assessed the vulnerabilities, furthermore, they discussed the open difficulties of event log encryption [8], including the incompleteness of encrypting event records and the shortcomings of using a single technique. Tillem et al proposed an Alpha method employing encryption protocols for process discovery, the proposed method may protect the privacy of users and software simultaneously [9]; Burattin et al addressed the outsourcing of PM analytics [10], where the confidentiality of event logs and resultant processes must be ensured by concealing sensitive data with symmetric or homomorphic encryption; Liu et al presented a trusted third party for public and private business process models [11]. To implement privacy regulations on event logs, a PM privacy-preserving system design based on an Attribute-Based Access Control (ABAC)authorization model is proposed in the literature [12].…”

Section: A Mainstream Privacy Preserving Methodsmentioning

confidence: 99%

PLI-Assess: A Behavior Profile-Based Approach for Privacy-Preserving Log Assessment

Wang,

Fang

2023

IEEE Access

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Event logs recorded during the execution of business processes provide a valuable starting point for process mining, and high-quality event logs can significantly enhance the quality of process mining. However, event logs often contain a substantial amount of sensitive and personal information. Therefore, the release of event logs should prioritize the model's quality while minimizing the risk of privacy exposure. Specifically, quantifying performance indicators between the original event logs and the released ones enables the operational goals. To date, privacy benefit and utility loss are two main target performance indicators, especially from the perspective of structural similarity comparison of mined process models. To the best of our knowledge, no study aims to measure the privacy-preserving performance indicators from the point of behavior differentiation between the original event logs and released ones, and this paper aims to address this problem. In this paper, we propose an approach that combines privacy benefit and utility loss measurements together to quantify the behavior differentiation between the original event logs and the corresponding released ones. Specifically, an approach of event log release mechanism that effectively combines behavior privacy gain and behavior utility loss is presented in this paper. Firstly, we discuss the challenges in scenarios where event data is released directly without any privacy preservation, and describe the various potential attacks that could occur when third-party businesses perform process mining techniques. Based on these potential attacks, we present a behavior differentiationbased event log release mechanism named PLI-Assess to combat these threats. Finally, we conduct experiments on four groups of practical event logs for comparisons with the baseline methods. The experimental results indicate general feasibility and shed light on the behavior trade-offs between privacy benefit and utility loss.

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Section: A Mainstream Privacy Preserving Methodsmentioning

confidence: 99%

PLI-Assess: A Behavior Profile-Based Approach for Privacy-Preserving Log Assessment

Wang,

Fang

2023

IEEE Access

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“…Only centralized event logs are considered failing to provide support for distributed event logs (R7 -Data). A cross-organizational setting is considered, e.g., by Tillem et al [50] who propose a secure processing protocol to execute the Alpha algorithm over distributed event logs (R7 -Data). However, their protocol does not mitigate attacks on confidentiality described in T4.…”

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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“…The first concern is how to ensure confidentiality and privacy during the computation of process mining operations, particularly in a cross-organizational setting. For example, Tillem et al [44,45] propose a distributed secure processing protocol for discovering process models using the alpha algorithm. Similarly, Elkoumy et al [19,20] propose a method for securely computing the DFG of an event log in an inter-organizational setting.…”

Section: Privacy-preserving Process Miningmentioning

confidence: 99%

Privacy-Preserving Directly-Follows Graphs: Balancing Risk and Utility in Process Mining

Elkoumy,

Pankova,

Dumas

2020

Preprint

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Process mining techniques enable organizations to analyze business process execution traces in order to identify opportunities for improving their operational performance. Oftentimes, such execution traces contain private information. For example, the execution traces of a healthcare process are likely to be privacy-sensitive. In such cases, organizations need to deploy Privacy-Enhancing Technologies (PETs) to strike a balance between the benefits they get from analyzing these data and the requirements imposed onto them by privacy regulations, particularly that of minimizing re-identification risks when data are disclosed to a process analyst. Among many available PETs, differential privacy stands out for its ability to prevent predicate singling out attacks and its composable privacy guarantees. A drawback of differential privacy is the lack of interpretability of the main privacy parameter it relies upon, namely epsilon. This leads to the recurrent question of how much epsilon is enough? This article proposes a method to determine the epsilon value to be used when disclosing the output of a process mining technique in terms of two business-relevant metrics, namely absolute percentage error metrics capturing the loss of accuracy (a.k.a. utility loss) resulting from adding noise to the disclosed data, and guessing advantage, which captures the increase in the probability that an adversary may guess information about an individual as a result of a disclosure. The article specifically studies the problem of protecting the disclosure of the so-called Directly-Follows Graph (DFGs), which is a process mining artifact produced by most process mining tools. The article reports on an empirical evaluation of the utility-risk trade-offs that the proposed approach achieves on a collection of 13 real-life event logs.

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Mining Encrypted Software Logs using Alpha Algorithm

Cited by 8 publications

References 0 publications

PLI-Assess: A Behavior Profile-Based Approach for Privacy-Preserving Log Assessment

PLI-Assess: A Behavior Profile-Based Approach for Privacy-Preserving Log Assessment

Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Privacy-Preserving Directly-Follows Graphs: Balancing Risk and Utility in Process Mining

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