Öffentliches und Geheimes

In the second half of 2009, the entire media agenda in Germany was dominated by the swine flu, next to the German Federal Parliament elections. Criticism was voiced in the media against the pharmaceutical industry for lobbying the nationwide swine flu vaccination, which was widely considered nothing but a product of the industry’s lobbying power. In order to avoid the usual interviews with lobbyists, we attempted to apply a new methodological framework approach in analysing lobbying processes and key stakeholders in the public sphere—and outside. Crucial to the diversified approach, which involves quantitative content analysis of media coverage and press releases, are interviews with journalists, lobbyists’ network analysis and internet research on biographies. Although a conspiracy could not be conclusively proven, the results do indicate associations that validate the criticism of the industry’s lobbying activities.     

Auf verschiedenen Meeren Jahrestagung der Deutschen Gesellschaft für Publizistik- und Kommunikationswissenschaft, 4.–6. Mai 2005 in Hamburg

Ohne Zusammenfassung     

Political pressures and the credibility of regulation: can profit sharing mitigate regulatory risk?

When price-cap rules determine the structure of prices for a long period, they suffer a credibility problem and introduce an element of risk especially if a firm’s profits are “too large”. Profit sharing may be seen as a device to pre-determine price adjustments and thus to decrease regulatory risk. We analyse the effects of profit sharing on the incentives to invest, using a real option approach. Absent credibility issues, a well designed profit sharing system may be neutral relative to a pure price cap. With regulatory risk, profit sharing is preferable to a pure price-cap one, if it intervenes for high enough profit levels.

An Approach to Security for Unstructured Big Data

Security of Big Data is a huge concern. In a broad sense, Big Data contains two types of data: structured and unstructured. Providing security to unstructured data is more difficult than providing security to structured data. In this paper, we have developed an approach to provide adequate security to unstructured data by considering types of data and their sensitivity levels. We have reviewed the different analytics methods of Big Data to build nodes of different types of data. Each type of data has been classified to provide adequate security and enhance the overhead of the security system. To provide security to a data node, and a security suite has been designed by incorporating different security algorithms. Those security algorithms collectively form a security suite which has been interfaced with the data node. Information on data sensitivity has been collected through a survey. We have shown through several experiments on multiple computer systems with varied configurations that data classification with respect to sensitivity levels enhances the performance of the system. The experimental results show how and in what amount the designed security suite reduces overhead and increases security simultaneously.