Ready or not, the digitalization of information has come and privacy is standing out there, possibly at stake. Although digital privacy is an identified priority in our society, few systematic, effective methodologies exist that deal with privacy threats thoroughly. This paper presents a comprehensive framework to model privacy threats in softwarebased systems. First, this work provides a systematic methodology to model privacy-specific threats. Analogous to STRIDE, an information flow oriented model of the system is leveraged to guide the analysis and to provide broad coverage. The methodology instructs the analyst on what issues should be investigated, and where in the model those issues could emerge. This is achieved by (i) defining a list of privacy threat types and (ii) providing the mappings between threat types and the elements in the system model. Second, this work provides an extensive catalogue of privacy-specific threat tree patterns that can be used to detail the threat analysis outlined above. Finally, this work provides the means to map the existing privacy-enhancing technologies (PETs) to the identified privacy threats. Therefore, the selection of sound privacy countermeasures is simplified.

This paper presents an approach based on machine learning to predict which components of a software application contain security vulnerabilities. The approach is based on text mining the source code of the components. Namely, each component is characterized as a series of terms contained in its source code, with the associated frequencies. These features are used to forecast whether each component is likely to contain vulnerabilities. In an exploratory validation with 20 Android applications, we discovered that a dependable prediction model can be built. Such model could be useful to prioritize the validation activities, e.g., to identify the components needing special scrutiny.

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Early identification of software vulnerabilities is essential in software engineering and can help reduce not only costs, but also prevent loss of reputation and damaging litigations for a software firm. Techniques and tools for software vulnerability prediction are thus invaluable. Most of the existing techniques rely on using component characteristic(s) (like code complexity, code churn) for the vulnerability prediction. In this position paper, we present a novel approach for vulnerability prediction that leverages on the analysis of raw source code as text, instead of using "cooked" features. Our initial results seem to be very promising as the prediction model achieves an average accuracy of 0.87, precision of 0.85 and recall of 0.88 on 18 versions of a large mobile application.

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Security patterns are well-known solutions to security-specific problems. They are often claimed to benefit designers without much security expertise. We have performed an empirical study to investigate whether the usage of security patterns by such an audience leads to a more secure design, or to an increased productivity of the designers. Our study involved 32 teams of master students enrolled in a course on software architecture, working on the design of a realisticallysized banking system. Irrespective of whether the teams were using security patterns, we have not been able to detect a difference between the two treatment groups. However, the teams prefer to work with the support of security patterns.