Privacy Challenges for Process Mining in Human-Centered Industrial Environments

Abstract

Operators in industrial manufacturing environments are under pressure to cope with increasing flexibility and complexity of work. The automation of manufacturing requires operators to adopt new techniques and shifts the focus from lowcomplexity repetitive tasks to dealing with the execution of highcomplexity tasks in cooperation with machines. The emergence of wearable technologies makes it possible to equip operators with miniaturized sensors that may be used to determine the physical and mental stress experienced by operators. Process mining technologies are suited to analyze such sensor data in the context of the manufacturing process with the ultimate goal of improving the operator's well-being through reorganization of work and the work place. However, the storage and processing of such highly personalized data comes with many privacy challenges. Whereas there are many potential benefits, such as improve the work environment, there are also many justified reasons for operators to oppose the processing of their data. Apart from employee concerns, data protection regulations, such as EU GDPR (Europe's General Data Protection Regulation), imposes many compliance challenges for the design of a process mining systems dealing with personal data. We contribute an analysis of the privacy challenges of using process mining on data recorded from sensorized operators in human-centered industrial environments. Guided by privacy research and the regulation imposed by the GDPR, we describe guidelines for privacy in process mining systems.

Performance Measurement in Sensorized Sociotechnical Manufacturing Environments

“…Finally, concerns regarding the privacy and security of data related to human activity must be addressed. The latter should be considered at the design stage and relevant guidelines need to be followed to address privacy issues of humancentered manufacturing environments for all phases of data lifecycle management (Mannhardt et al [15]). The first two challenges require further research on context information management, which is now recognized as of high importance in modern enterprise information systems and as such it is also relevant to the assessment of human performance in workplaces [16].…”

HCI International 2019 – Late Breaking Papers

“…In addition to the traditional digital information originating from production systems and the environment, the operator themselves was sensorized with wearable devices, depth cameras, video and audio. Whilst this richer data sets provided the means of creating more accurate understanding of the work context at hand, serious concerns regarding privacy were raised [22][23], which consequently had an impact on the trust operators would have in the service and affecting their acceptance. • Integration with the Work Practice.…”

A Taxonomy for Combining Activity Recognition and Process Discovery in Industrial Environments

“…However, only very few applications of process mining are reported within the manufacturing domain [13,23]. One reason for this gap might be that in many industrial environments, much of the manual work is not precisely captured in databases or logs.…”

“…: due to poor performance, an operators employment is terminated). The body of research covered, with exception of [3], focuses very much on the opportunities of processing the data collated, whilst disregarding the potential threats to the operators well-being [23]. To address the challenges, governments have intervened wtih regulatory frameworks to safeguard the privacy of the user, such as the General Data Protection Regulation (GDPR) that attempts to place the user in control of their digital selves.…”