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Process mining techniques enable organizations to gain insights into their business processes through the analysis of execution records (event logs) stored by information systems. While most process mining efforts focus on…
Information systems support the execution of business processes. The event logs of these executions generally contain sensitive information about customers, patients, and employees. The corresponding privacy challenges can be addressed by…
Data mining has made broad significant multidisciplinary field used in vast application domains and extracts knowledge by identifying structural relationship among the objects in large data bases. Privacy preserving data mining is a new…
We introduce PrivPy, a practical privacy-preserving collaborative computation framework, especially optimized for machine learning tasks. PrivPy provides an easy-to-use and highly compatible Python programming front-end which supports…
Process mining is a new emerging research trend over the last decade which focuses on analyzing the processes using event log and data. The raising integration of information systems for the operation of business processes provides the…
Anonymization of event logs facilitates process mining while protecting sensitive information of process stakeholders. Existing techniques, however, focus on the privatization of the control-flow. Other process perspectives, such as roles,…
One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be…
Daily, massive volume of data are produced due to the internet of things' rapid development, which has now permeated the healthcare industry. Recent advances in data mining have spawned a new field of a study dubbed privacy-preserving data…
Open-source process mining provides many algorithms for the analysis of event data which could be used to analyze mainstream processes (e.g., O2C, P2P, CRM). However, compared to commercial tools, they lack the performance and struggle to…
The capability of process mining techniques in providing extensive knowledge and insights into business processes has been widely acknowledged. Process mining techniques support discovering process models as well as analyzing process…
Protecting personal data about individuals, such as event traces in process mining, is an inherently difficult task since an event trace leaks information about the path in a process model that an individual has triggered. Yet, prior…
Event logs capture the execution of business processes in terms of executed activities and their execution context. Since logs contain potentially sensitive information about the individuals involved in the process, they should be…
Data mining deals with automatic extraction of previously unknown patterns from large amounts of data. Organizations all over the world handle large amounts of data and are dependent on mining gigantic data sets for expansion of their…
Privacy-preserving computation techniques like homomorphic encryption (HE) and secure multi-party computation (SMPC) enhance data security by enabling processing on encrypted data. However, the significant computational and CPU-DRAM data…
Process mining provides techniques to improve the performance and compliance of operational processes. Although sometimes the term "workflow mining" is used, the application in the context of Workflow Management (WFM) and Business Process…
Data mining is also being useful to give solutions for invasion finding and auditing. While data mining has several applications in protection, there are also serious privacy fears. Because of email mining, even inexperienced users can…
Conformance checking, one of the main process mining operations, aims to identify discrepancies between a process model and an event log. The model represents the expected behaviour, whereas the event log represents the actual process…
With the advent of big data, periodic pattern mining has demonstrated significant value in real-world applications, including smart home systems, healthcare systems, and the medical field. However, advances in network technology have…
Privacy Preserving Data Mining is a method which ensures privacy of individual information during mining. Most important task involves retrieving information from multiple data bases which is distributed. The data once in the data warehouse…
Pseudonymisation provides the means to reduce the privacy impact of monitoring, auditing, intrusion detection, and data collection in general on individual subjects. Its application on data records, especially in an environment with…