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Voice assistants have become embedded in people's private spaces and domestic lives where they gather enormous amounts of personal information which is why they evoke serious privacy concerns. The paper reports the findings from a mixed-method study with 65 digital natives, their attitudes to privacy and actual and intended behaviour in privacy-sensitive situations and contexts. It also presents their recommendations to governments or organisations with regard to protecting their data. The results show that the majority are concerned about privacy but are willing to disclose personal data if the benefits outweigh the risks. The prevailing attitude is one characterised by uncertainty about what happens with their data, powerlessness about controlling their use, mistrust in big tech companies and uneasiness about the lack of transparency. Few take steps to self-manage their privacy, but rely on the government to take measures at the political and regulatory level. The respondents, however, show scant awareness of existing or planned legislation such as the GDPR and the Digital Services Act, respectively. A few participants are anxious to defend the analogue world and limit digitalization in general which in their opinion only opens the gate to surveillance and misuse. [ABSTRACT FROM AUTHOR]

Modern handheld devices can act as “magic lenses” for public displays and enable camera-based real-time interaction with their contents, thus allowing for manifold interactive applications in public space. To learn more about the characteristics of common techniques and to provide guidelines for new ones, a comparative user study was conducted. With regard to two basic task types (selection and translation) and two typical devices (smartphone and tablet), three interaction techniques in-depth were evaluated: direct touch-based input (Touch), cross-hair targeting (Target), and a new technique based on a dynamic cross-hair on the screen that snaps to visible nearby screen objects (Snap Target). The study results indicate that successful interaction with the Touch technique strongly depends on the usage context: Although Touch enables fast selection, it incurs many errors when small targets have to be selected on a smartphone. Target supports such difficult selections better, but Snap Target proved to be most robust in both investigated task types. Also, users stated that they felt best supported by the latter technique. Cross-hair-based techniques, especially Snap Target, were found to be well suitable for scenarios where several device types need to be supported under similar conditions. Implications and further work are discussed. [ABSTRACT FROM PUBLISHER]