Electronic payment protocols provide secure service for electronic commerce transactions and protect private information from malicious entities in a network. Formal methods have been introduced to verify the security of electronic payment protocols; however, these methods concentrate on the accountability and fairness of the protocols, without considering the impact caused by timeliness. To make up for this deficiency, we present a formal method to analyze the security properties of electronic payment protocols, namely, accountability, fairness and timeliness. We add a concise time expression to an existing logical reasoning method to represent the event time and extend the time characteristics of the logical inference rules. Then, the Netbill protocol is analyzed with our formal method, and we find that the fairness of the protocol is not satisfied due to the timeliness problem. The results illustrate that our formal method can analyze the key properties of electronic payment protocols. Furthermore, it can be used to verify the time properties of other security protocols.

Threats to a society and its social infrastructure are inevitable and endanger human life and welfare. Resilience is a core concept to cope with such threats in strengthening risk management. A resilient system adapts to an incident in a timely manner before it would result in a failure. This paper discusses the secondary use of personal data as a key element in such conditions and the relevant process mining in order to reduce IT risk on safety. It realizes completeness for such a proof on data breach in an acceptable manner to mitigate the usability problem of soundness for resilience. Acceptable soundness is still required and realized in our scheme for a fundamental privacy-enhancing trust infrastructure. Our proposal achieves an IT baseline protection and properly treats personal data on security as Ground Truth for deriving acceptable statements on data breach. An important role plays reliable broadcast by means of the block chain. This approaches a personal IT risk management with privacy-enhancing cryptographic mechanisms and Open Data without trust as belief in a single-point-of-failure. Instead it strengthens communities of trust.

This paper proposes a method for reinforcing noun countability prediction, which plays a crucial role in demarcating correct determiners in machine translation and error detection. The proposed method reinforces countability prediction by introducing a novel heuristics called one countability per discourse. It claims that when a noun appears more than once in a discourse, all instances will share identical countability. The basic idea of the proposed method is that mispredictions can be corrected by efficiently using one countability per discourse heuristics. Experiments show that the proposed method successfully reinforces countability prediction and outperforms other methods used for comparison. In addition to its performance, it has two advantages over earlier methods: (i) it is applicable to any countability prediction method, and (ii) it requires no human intervention to reinforce countability prediction.