We argue for the use of Privacy Dashboards as enablers for privacy-enabled data-driven business models. Specifically, while dashboards are succesful instruments in business intelligence tools, their use in privacy protection is far less well-understood. Addressing this problem at the technical level, this paper provides a classification scheme for Privacy Dashboards and elaborates on the current state of the art to draw a research agenda for designing Privacy Dashboards that cater to users' desire of control and businesses' need for data collection and usage.

This paper reports on ongoing work on SWAT, a new toolkit for security workflow analysis. SWAT provides a platform for the realization and testing of well-founded methods to detect information leaks in workflows, both for the workflow certification and for audit based upon the execution traces. Besides presenting the SWAT's functionality and high-level architecture, an example illustrates its operation.

Business processes and services can more flexibly be combined when based upon standards. However, such flexible compositions practically always contain vulnerabilities, which imperil the security and dependability of processes. Vulnerability management tools require patterns to find or monitor vulnerabilities. Such patterns have to be derived from vulnerability types. Existing analysis methods such as attack trees and FMEA result in such types, yet require much experience and provide little guidance during the analysis. Our main contribution is ATLIST, a new vulnerability analysis method with improved transferability. Especially in service-oriented architectures, which employ a mix of established web technologies and SOA-specific standards, previously observed vulnerability types and variations thereof can be found. Therefore, we focus on the detection of known vulnerability types by leveraging previous vulnerability research. A further contribution in this respect is the, to the best of our knowledge, most comprehensive compilation of vulnerability information sources to date. We present the method to search for vulnerability types in SOA-based business processes and services. Also, we show how patterns can be derived from these types, so that tools can be employed. An additional contribution is a case study, in which we apply the new method to an SOA-based business process scenario.

While log data are being increasingly used as digital evidence in judicial disputes, the extent to which existing secure logging protocols used to collect log data fulfill the legal requirements for admissible evidence remain largely unclear. We elucidate the necessary secure requirements for digital evidence and extensively survey the state of the art secure logging protocols,thereby demonstrating that none of the current proposals fulfills the necessary conditions for admissible evidence.

Vulnerabilities in operating systems and Web applications have been and are being put into various classifications, leading to a better understanding of their causes and effects, and to improved vulnerability management tool support. In a service-oriented architecture (SOA), additional vulnerabilities exist in the implementations of new standards such as BPEL and SOAP. Attackers can exploit these vulnerabilities to interfere with the business processes, which are executed as orchestration of services. We describe our approach and ongoing work of creating a SOA vulnerability classification.

To-date, security analysis techniques focus on the explicit access to data, thereby neglecting information flows happening over covert channels. As a result, critical business software applications and their deployment may be labeled secure, whereas in fact they are not. We present ongoing research towards information flow forensics, a novel approach for the a-posteriori detection of information flow. We motivate our work by illustrating the implications of illicit information flow in different software application scenarios and demonstrate why current approaches fall short of effectively enforcing information flow policies in many cases. We show that information flow forensics can mitigate these drawbacks and outline some interesting research challenges involved in its realization.

We investigate a mechanism for secure remote logging to improve privacy guarantees in dynamic systems. Using an extended threat model for privacy, we first describe outer and inner privacy: outer privacy denotes the traditional attacker model for privacy where identity management systems control the collection of personal, observable information; inner privacy denotes the threat posed by an attacker who attempts to get hold of private log data by tampering with a device. While privacy-enhancing technologies should take outer and inner privacy into account, there is, to our knowledge, no approach for inner privacy, in particular for dynamic systems. To this end, we develop protocols to address inner privacy based on secure logging. Our approach accounts for the capacity limitations of resource-poor devices in dynamic systems, as it allows for the remote storage of log data, while fulfilling its security guarantees. Furthermore, our approach can be smoothly integrated into identity management systems to combine outer and inner privacy. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.