activity
20182020
most citedPrivacy-Preserving Directly-Follows Graphs: Balancing Risk and Utility in Process Mining

2 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CR20202 cited

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

Gamal Elkoumy, Alisa Pankova, Marlon Dumas

Process mining techniques enable organizations to analyze business process execution traces in order to identify opportunities for improving their operational performance. Oftentim…

cs.CR2019

Secure Multi-Party Computation for Inter-Organizational Process Mining

Gamal Elkoumy, Stephan A. Fahrenkrog-Petersen, Marlon Dumas +3

Process mining is a family of techniques for analysing business processes based on event logs extracted from information systems. Mainstream process mining tools are designed for i…

cs.CR20191 cited

Interpreting Epsilon of Differential Privacy in Terms of Advantage in Guessing or Approximating Sensitive Attributes

Peeter Laud, Alisa Pankova

There are numerous methods of achieving -differential privacy (DP). The question is what is the appropriate value of , since there is no common agreement on a "sufficiently s…

cs.CR2019

Business Process Privacy Analysis in Pleak

Aivo Toots, Reedik Tuuling, Maksym Yerokhin +8

Pleak is a tool to capture and analyze privacy-enhanced business process models to characterize and quantify to what extent the outputs of a process leak information about its inpu…

cs.CR2018

Achieving Differential Privacy using Methods from Calculus

Peeter Laud, Alisa Pankova, Martin Pettai

We introduce derivative sensitivity, an analogue to local sensitivity for continuous functions. We use this notion in an analysis that determines the amount of noise to be added to…