Impact assessment (IA) is a key method for the legislator to evaluate policies, norms or regulations currently under development. Experts use IA to gather and analyze input from many individuals to obtain clear problem statements, estimations regarding policies etc., and use this information to compare policy alternatives. Currently, the opinions, expertise etc. gathered for IA need to be structured by hand. Thus, the analysis steps of IA are time-consuming, and IA does not scale with the number of persons involved. In this paper, we introduce a collaborative approach for IA. Based on a Web 2.0 architecture, we let a community of individuals derive the potential, downsides and design alternatives of policies collaboratively. Our approach guides individuals through the process of creating structured input. Our approach is fully implemented, and we have evaluated it together with legal experts by means of a small pilot study. Our evaluation indicates that the acceptance of a Web 2.0-based IA application strongly depends on the user interface. More profoundly, it shows that our approach can be an important tool for future IA.
In past years, many anonymization schemes, anonymity notions, and anonymity measures have been proposed. When designing information systems that feature anonymity, choosing a good approach is a very important design choice. While experiments comparing such approaches are enlightening, carrying out such experiments is a complex task and is labor-intensive. To address this issue, we propose the framework FACTS for the experimental evaluation of anonymization schemes. It lets researchers implement their approaches against interfaces and other standardizations that we have devised. Users can then define benchmark suites that refer to those implementations. FACTS gives way to comparability, and it includes many useful features, e.g., easy sharing and reproduction of experiments. We evaluate FACTS (a) by specifying and executing a comprehensive benchmark suite for data publishing and (b) by means of a user study. Core results are that FACTS is useful for a broad range of scenarios, that it allows to compare approaches with ease, and that it lets users share and reproduce experiments.
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