Abstract
Existing access control mechanisms are not sufficient for data protection. They are only preventive and cannot guarantee that data is accessed for the intended purpose. This paper proposes a novel approach for multi-perspective conformance checking which considers the control-flow, data and privacy perspectives of a business process simultaneously to find the context in which data is processed. In addition to detecting deviations in each perspective, the approach is able to detect hidden deviations where non-conformity relates to either a combination of two or all three aspects of a business process. The approach has been implemented in the open source ProM framework and was evaluated through controlled experiments using synthetic logs of a simulated real-life process.
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References
Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Towards robust conformance checking. In: zur Muehlen, M., Su, J. (eds.) BPM 2010. LNBIP, vol. 66, pp. 122–133. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20511-8_11
Aggarwal, C.C.: Data Mining. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14142-8
Alizadeh, M., Lu, X., Fahland, D., Zannone, N., van der Aalst, W.: Linking data and process perspectives for conformance analysis. Comput. Secur. 73, 172–193 (2018)
de Leoni, M., van der Aalst, W.M.P.: Aligning event logs and process models for multi-perspective conformance checking: an approach based on integer linear programming. In: Daniel, F., Wang, J., Weber, B. (eds.) Business Process Management, pp. 113–129. Springer, Berlin Heidelberg, Berlin, Heidelberg (2013)
de Leoni, M., van der Aalst, W.M.P., van Dongen, B.F.: Data- and resource-aware conformance checking of business processes. In: Abramowicz, W., Kriksciuniene, D., Sakalauskas, V. (eds.) Business Information Systems, pp. 48–59. Springer, Heidelberg (2012)
Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407–437 (2015). https://doi.org/10.1007/s00607-015-0441-1
Mannhardt, F., Koschmider, A., Baracaldo, N., Weidlich, M., Michael, J.: Privacy-preserving process mining differential privacy for event logs. Bus. Inf. Syst. Eng. 61, 1–20 (2019)
Michael, J., Koschmider, A., Mannhardt, F., Baracaldo, N., Rumpe, B.: User-centered and privacy-driven process mining system design for IoT. In: Cappiello, C., Ruiz, M. (eds.) Information Systems Engineering in Responsible Information Systems, pp. 194–206. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21297-1_17
Perry, J.W., Kent, A., Berry, M.M.: Machine literature searching x. Machine language; factors underlying its design and development. Am. Docum. 6(4), 242–254 (1955)
Petković, M., Prandi, D., Zannone, N.: Purpose control: did you process the data for the intended purpose? In: Jonker, W., Petković, M. (eds.) SDM 2011. LNCS, vol. 6933, pp. 145–168. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23556-6_10
Pika, A., Wynn, M.T., Udiono, S., Ter Hofstede, A.H.M., van der Aalst, W.M.P., Reijers, H.A.: Privacy-preserving process mining in healthcare. Int. J. Environ. Res. Public Health 17 (2020)
Rafiei, M., van der Aalst, W.M.P.: Privacy-preserving data publishing in process mining. CoRR abs/2101.02627 (2021)
Zhang, S., Genga, L., Dekker, L., Nie, H., Lu, X., Duan, H., Kaymak, U.: Towards multi-perspective conformance checking with aggregation operations. In: Lesot, M.-J., et al. (eds.) IPMU 2020. CCIS, vol. 1237, pp. 215–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50146-4_17
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The author has received funding within the BPR4GDPR project from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 787149.
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Mozafari Mehr, A.S., de Carvalho, R.M., van Dongen, B. (2021). Detecting Privacy, Data and Control-Flow Deviations in Business Processes. In: Nurcan, S., Korthaus, A. (eds) Intelligent Information Systems. CAiSE 2021. Lecture Notes in Business Information Processing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-79108-7_10
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