Measuring Privacy Compliance with Process Specifications

Bănescu, Sebastian; Zannone, Nicola

doi:10.1109/metrisec.2011.10

Cited by 12 publications

(12 citation statements)

References 23 publications

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“…Many existing precision metrics (e.g., [25,30,34]) do not explicitly take into account possible deviations between the behavior observed in the event log with the behavior modeled in the models, while many case studies show that such deviations often occur in practice (e.g., [11,16,19,20,23,31,35,36]). Thus, these metrics might be biased due to unfitting logs and models.…”

Section: Introductionmentioning

confidence: 99%

Measuring precision of modeled behavior

Adriansyah

Muñoz-Gama

Carmona

et al. 2014

Inf Syst E-Bus Manage

125

100

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Conformance checking techniques compare observed behavior (i.e., event logs) with modeled behavior for a variety of reasons. For example, discrepancies between a normative process model and recorded behavior may point to fraud or inefficiencies. The resulting diagnostics can be used for auditing and compliance management. Conformance checking can also be used to judge a process model automatically discovered from an event log. Models discovered using different process discovery techniques need to be compared objectively. These examples illustrate just a few of the many use cases for aligning observed and modeled behavior. Thus far, most conformance checking techniques focused on replay fitness, i.e., the ability to reproduce the event log. However, it is easy to construct models that allow for lots of behavior (including the observed behavior) without being precise.In this paper, we propose a method to measure precision of process models, given their event logs by first aligning the logs to the models. This way, the measurement is not sensitive to non-fitting executions and more accurate values can be obtained for non-fitting logs. Furthermore, we introduce several variants of the technique to deal better with incomplete logs and reduce possible bias due to behavioral property of process models. The approach has been implemented in the ProM 6 framework and tested against both artificial and real-life cases. Experiments show that the approach is robust to noise and applicable to handle logs and models of real-life complexity.

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Section: Introductionmentioning

confidence: 99%

Measuring precision of modeled behavior

Adriansyah

Muñoz-Gama

Carmona

et al. 2014

Inf Syst E-Bus Manage

125

100

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show abstract

“…The process for the identification of replacements and estimation of their costs can be supported by the use of metrics for assessing the similarity degree between activities. For instance, the work in [17] uses the Latent Semantic Analysis (LSA) [18], a semantic relatedness metric, to assess the similarity between two activities based on their name. Ontology alignment techniques [19,20] can be also employed to assess the similarity degree between two activities.…”

Section: Guidelines For Setting Cost Functionmentioning

confidence: 99%

“…In particular, edit distance metrics are used to map "almost valid" traces into the predictable (closest valid) trace. In [17] edit distance metrics have been enhanced to measure the privacy distance between expected and actual user behavior. The problem of these metrics is that they cannot be applied to analyze logs against complex process models, for instance including loops.…”

Section: Related Workmentioning

confidence: 99%

Controlling Break-the-Glass through Alignment

Adriansyah

Zannone

2013

2013 International Conference on Social Computing

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“…The role by the user who executed the task is another factor for characterizing privacy [5]. A role describes job Privacy Analysis of User Behavior Using Alignments functions and responsibilities within an organization and is usually associated with the access rights necessary to achieve assigned duties.…”

Section: Quantifying Privacymentioning

confidence: 99%

Privacy Analysis of User Behavior Using Alignments

Adriansyah

Zannone

2013

It – Information Technology

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Privacy is becoming a urgent issue in information systems nowadays because of the stringent requirements imposed by data protection regulations. Traditional security approaches based on access control and authorization are not adequate to address these requirements. The underlying fundamental problem is that those approaches are preventive and thus they are not able to deal with exceptions. In this paper, we present a practical privacy framework that shifts the problem of preventing infringements into a problem of detecting infringements. The framework is based on systematic log auditing, use of patterns and privacy metrics to detect and quantify infringements.

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Measuring Privacy Compliance with Process Specifications

Cited by 12 publications

References 23 publications

Measuring precision of modeled behavior

Measuring precision of modeled behavior

Controlling Break-the-Glass through Alignment

Privacy Analysis of User Behavior Using Alignments

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