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Trust and Distrust: On Sense and Nonsense in Big Data

Abstract : Big data is an appealing source and often perceived to bear all sorts of hidden information. Filtering out the gemstones of information besides the rubbish that is equally easy to “deduce” is, however, a nontrivial issue. This position paper will open with the motivating problem of risk estimation for an enterprise, using big data. Our illustrative context here is the synERGY project (“security for cyber-physical value networks Exploiting smaRt Grid sYstems”), which serves as a case study to show the (unexplored) potential, application and difficulties of using big data in practice. The paper first goes into a list of a few general do’s and don’ts about data analytics, and then digs deeper into (semi-) automated risk evaluation via a statistical trust model. Ideally, the trust and hence risk assessment should be interpretable, justified, up-to-date and comprehensible in order to provide a maximum level of information with minimal additional manual effort. The ultimate goal of projects like synERGY is to establish trust in a system, based on observed behavior and its resilience to anomalies. This calls for a distinction of “normal” (in the sense of behavior under expected working conditions) from “abnormal” behavior, and trust can intuitively be understood as the (statistical) expectation of “normal” behavior.
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Stefan Rass, Andreas Schorn, Florian Skopik. Trust and Distrust: On Sense and Nonsense in Big Data. Eleni Kosta; Jo Pierson; Daniel Slamanig; Simone Fischer-Hübner; Stephan Krenn. Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data : 13th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2 International Summer School, Vienna, Austria, August 20-24, 2018, Revised Selected Papers, AICT-547, Springer International Publishing, pp.81-94, 2019, IFIP Advances in Information and Communication Technology, 978-3-030-16743-1. ⟨10.1007/978-3-030-16744-8_6⟩. ⟨hal-02271659⟩

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