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    User Experiences of the NZ COVID Tracer App in New School of ManagementZealand: Thematic Analysis of Interviews
    (JMIR Publications, 2021-09-08) Tretiakov A; Hunter I
    BACKGROUND: For mobile app-based COVID-19 contact tracing to be fully effective, a large majority of the population needs to be using the app on an ongoing basis. However, there is a paucity of studies of users, as opposed to potential adopters, of mobile contact tracing apps and of their experiences. New Zealand, a high-income country with western political culture, was successful in managing the COVID-19 pandemic, and its experience is valuable for informing policy responses in similar contexts. OBJECTIVE: This study asks the following research questions: (1) How do users experience the app in their everyday contexts? and (2) What drives the use of the app? METHODS: Residents of New Zealand's Auckland region, which encompasses the country's largest city, were approached via Facebook, and 34 NZ COVID Tracer app users were interviewed. Interview transcripts were analyzed using thematic analysis. RESULTS: Interviews ranged in duration from 15 to 50 minutes. Participants ranged in age from those in their late teens to those in their early sixties. Even though about half of the participants identified as White New Zealanders of European origin, different ethnicities were represented, including New Zealanders of South Pacific, Indian, Middle Eastern, South American, and Southeast Asian descent. Out of 34 participants, 2 (6%) identified as Māori (Indigenous New Zealanders). A broad range of careers were represented, from top-middle management to health support work and charity work. Likewise, educational backgrounds ranged broadly, from high school completion to master's degrees. Out of 34 participants, 2 (6%) were unemployed, having recently lost their jobs because of the pandemic. The thematic analysis resulted in five major themes: perceived benefits, patterns of use, privacy, social influence, and need for collective action. Benefits of using the app to society in general were more salient to the participants than immediate health benefits to the individual. Use, however, depended on the alert level and tended to decline for many participants at low alert levels. Privacy considerations played a small role in shaping adoption and use, even though the participants were highly aware of privacy discourse around the app. Participants were aware of the need for high levels of adoption and use of the app to control the pandemic. Attempts to encourage others to use the app were common, although not always successful. CONCLUSIONS: Appeals to civic responsibility are likely to drive the use of a mobile contact tracing app under the conditions of high threat. Under the likely scenario of COVID-19 remaining endemic and requiring ongoing vigilance over the long term, other mechanisms promoting the use of mobile contact tracing apps may be needed, such as offering incentives. As privacy is not an important concern for many users, flexible privacy settings in mobile contact tracing apps allowing users to set their optimal levels of privacy may be appropriate.
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    Scalable, high-performance, and generalized subtree data anonymization approach for Apache Spark
    (MDPI (Basel, Switzerland), 2021-03-03) Bazai SU; Jang-Jaccard J; Alavizadeh H; Guitart J
    Data anonymization strategies such as subtree generalization have been hailed as techniques that provide a more efficient generalization strategy compared to full-tree generalization counterparts. Many subtree-based generalizations strategies (e.g., top-down, bottom-up, and hybrid) have been implemented on the MapReduce platform to take advantage of scalability and parallelism. However, MapReduce inherent lack support for iteration intensive algorithm implementation such as subtree generalization. This paper proposes Distributed Dataset (RDD)-based implementation for a subtree-based data anonymization technique for Apache Spark to address the issues associated with MapReduce-based counterparts. We describe our RDDs-based approach that offers effective partition management, improved memory usage that uses cache for frequently referenced intermediate values, and enhanced iteration support. Our experimental results provide high performance compared to the existing state-of-the-art privacy preserving approaches and ensure data utility and privacy levels required for any competitive data anonymization techniques.
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    Maintaining Social Licence for Government Use of False Social Media Personas
    (Centre for Defence and Security Studies, Massey University, 12/06/2020) Cleaver O; Nicklin G
    Governmental collection of unprotected information from social media platforms via social media intelligence (SOCMINT) techniques enable the detection and prevention of unlawful and malicious activity for law enforcement purposes. Relatively new, these techniques have come under public scrutiny. Recognised as valuable tools for security, law enforcement and regulatory agencies, how government SOCMINT policies align with public expectations is less clear. This article addresses the gap by comparing New Zealand public expectations about the use of false social media personas as a SOCMINT technique with government policies. 248 individuals were surveyed, establishing initial understandings of public expectations. Findings were compared with policies of key oversight agencies - the State Services Commission and Privacy Commission. This article argues that to maintain social licence, governments using false social media personas need to appropriately balance public protection with personal privacy interests. Transparent policy frameworks are needed to maintain trust and confidence in SOCMINT governance.