No abstract

The allocation of resources in a business process determines the trade-off between cycle time and resource cost. A higher resource utilization leads to lower cost and higher cycle time, while a lower resource utilization leads to higher cost and lower waiting time. In this setting, this paper presents a multi-objective optimization approach to compute a set of Pareto-optimal resource allocations for a given process concerning cost and cycle time. The approach heuristically searches through the space of possible resource allocations using a simulation model to evaluate each allocation. Given the high number of possible allocations, it is imperative to prune the search space. Accordingly, the approach incorporates a method that selectively perturbs a resource utilization to derive new candidates that are likely to Pareto-dominate the already explored ones. The perturbation method relies on two indicators: resource utilization and resource impact, the latter being the contribution of a resource to the cost or cycle time of the process. Additionally, the approach incorporates a ranking method to accelerate convergence by guiding the search towards the resource allocations closer to the current Pareto front. The perturbation and ranking methods are embedded into two search meta-heuristics, namely hill-climbing and tabu-search. Experiments show that the proposed approach explores fewer resource allocations to compute Pareto fronts comparable to those produced by a well-known genetic algorithm for multi-objective optimization, namely NSGA-II.

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 inputs. Pleak incorporates an extensible set of analysis plugins, which enable users to inspect potential leakages at multiple levels of detail.

No abstract

Privacy regulations, such as GDPR, impose strict requirements to organizations that store and process private data. Privacyenhancing technologies (PETs), such as secure multi-party computation and differential privacy, provide mechanisms to perform computations over private data and to protect the disclosure of private data and derivatives thereof. When PETs are used to protect individual computations or disclosures, their privacy properties and their effect on the utility of the disclosed data can be straightforwardly asserted. However, when multiple PETs are used as part of a complex and possibly interorganizational business process, it becomes non-trivial for analysts to fully grasp the guarantees that the combined set of PETs provide overall. This article presents a multi-level approach to analyze privacy properties of business processes that rely on PETs to protect private data. The approach is embodied in an open-source toolset, Pleak, that allows analysts to capture privacy-enhanced business process models and to characterize and quantify to what extent the outputs of a process leak information about its inputs. Pleak incorporates an extensible set of analysis plugins, which enable users to inspect potential leakages at multiple levels of detail.