We present methods to find (significant) frequent synchronous patterns in event sequences, using a graded notion of synchrony that captures both the number of instances of a pattern as well as the precision of synchrony of its constituting events. Since transferring earlier work (using a binary notion of synchrony) poses certain problems, we opt for an efficient approximation scheme to compute the pattern support. Furthermore, we transfer methods for filtering for significant and removing induced patterns, which require adaptations. Finally, we demonstrate the effectiveness of our approach with experiments on a large number of data sets with injected synchronous patterns.

The paper discusses means to identify potential impacts of data flows on customers' security, and privacy during online payments. The main objectives of our research are looking into the evolution of cybercrime new trends of online payments and detection, more precisely the usage of mobile phones, and describing methodologies for digital trace identification in data flows for potential online payment fraud. The paper aims to identify potential actions for identity theft while conducting the Reconnaissance step of the kill chain, and documenting a forensic methodology for guidance and further data collection for law enforcement bodies. Moreover, a secondary objective of the paper is to identify, from a user's perspective, transparency issues of data sharing among involved parties for online payments. We thus declare the transparency analysis as the incident triggering a forensic examination. Hence, we devise a semi-automated traffic analysis approach, based on previous work, to examine data flows, and data exchanged among parties in online payments. For this, the main steps are segmenting traffic generated by the process payment, and other sources, subsequently, identifying data streams in the process. We conduct three tests which include three different payment gateways: PayPal, Klarna-sofort, and Amazon Pay. The experiment setup requires circumventing TLS encryption for the correct identification of forensic data types in TCP/IP traffic, and potential data leaks. However, it requires no extensive expertise in mobile security for its installation. In the results, we identified some important security vulnerabilities from some payment APIs that pose financial and privacy risks to the marketplace's customers. CCS CONCEPTS• Applied computing → Evidence collection, storage and analysis; Network forensics; • Security and privacy → Economics of security and privacy. This work is licensed under a Creative Commons Attribution International 4.0 License.

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Due to the increasing number of complaints alleging privacy violations against companies to data protection authorities, the translation of business goals to system design goals and the subsequent consequences for customers’ privacy poses a challenge for many companies. For this reason, there is a need to bridge the economics of privacy and threats to privacy. To this end, our work relies on the concept of privacy as contextual integrity. This framework defines privacy as appropriate information flows subjected to social norms within particular social contexts or spheres. In this paper, we introduce a preliminary version of a semantic model which aims to relate and provide understanding on how well-established business goals may affect their customers’ privacy by designing IoT devices with permission access, data acquired by sensors, among other factors. Finally, we provide a use case application showing how to use the semantic model. The model aims to be an educational tool for professionals in business informatics during the modeling and designing process of a product which may gather sensitive data or may infer sensitive information, giving an understanding of the interaction of the product and its footprint with diverse actors (humans or machines). In the future, a further complete model of the presented may also target other groups, such as law enforcement bodies, as part of their educational training in such systems.