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1. Creel Surveys for Social-Ecological-Systems Focused Fisheries Management

   https://doi.org/10.1080/23308249.2020.1869696  

   Nieman, Chelsey L.; Iwicki, Carolyn; Lynch, Abigail J.; Sass, Greg G.; Solomon, Christopher T.; Trudeau, Ashley; van Poorten, Brett (January 2021, Reviews in Fisheries Science & Aquaculture)

   null (Ed.)

   Recreational fisheries are social-ecological systems (SES), and knowledge of human dimensions coupled with ecology are critically needed to understand their system dynamics. Creel surveys, which typically occur in-person and on-site, serve as an important tool for informing fisheries management. Recreational fisheries creel data have the potential to inform large-scale understanding of social and ecological dynamics, but applications are currently limited by a disconnect between the questions posed by social-ecological researchers and the methods in which surveys are conducted. Although innovative use of existing data can increase understanding of recreational fisheries as SES, creel surveys should also adapt to changing information needs. These opportunities include using the specific temporal and spatial scope of creel survey data, integrating these data with alternative data sources, and increasing human dimensions understanding. This review provides recommendations for adapting survey design, implementation, and analysis for SES-focused fisheries management. These recommendations are: (1) increasing human dimensions knowledge; (2) standardization of surveys and data; (3) increasing tools and training available to fisheries scientists; and (4) increasing accessibility and availability of data. Incorporation of human dimensions information into creel surveys will increase the ability of fisheries management to regulate these important systems from an integrated SES standpoint. 

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2. A Tale of Two Communities: Privacy of Third Party App Users in Crowdsourcing - The Case of Receipt Transcription

   https://doi.org/10.1145/3610044  

   Pei, Weiping; Likhtenshteyn, Yanina; Yue, Chuan (September 2023, Proceedings of the ACM on Human-Computer Interaction)

   Mobile and web apps are increasingly relying on the data generated or provided by users such as from their uploaded documents and images. Unfortunately, those apps may raise significant user privacy concerns. Specifically, to train or adapt their models for accurately processing huge amounts of data continuously collected from millions of app users, app or service providers have widely adopted the approach of crowdsourcing for recruiting crowd workers to manually annotate or transcribe the sampled ever-changing user data. However, when users' data are uploaded through apps and then become widely accessible to hundreds of thousands of anonymous crowd workers, many human-in-the-loop related privacy questions arise concerning both the app user community and the crowd worker community. In this paper, we propose to investigate the privacy risks brought by this significant trend of large-scale crowd-powered processing of app users' data generated in their daily activities. We consider the representative case of receipt scanning apps that have millions of users, and focus on the corresponding receipt transcription tasks that appear popularly on crowdsourcing platforms. We design and conduct an app user survey study (n=108) to explore how app users perceive privacy in the context of using receipt scanning apps. We also design and conduct a crowd worker survey study (n=102) to explore crowd workers' experiences on receipt and other types of transcription tasks as well as their attitudes towards such tasks. Overall, we found that most app users and crowd workers expressed strong concerns about the potential privacy risks to receipt owners, and they also had a very high level of agreement with the need for protecting receipt owners' privacy. Our work provides insights on app users' potential privacy risks in crowdsourcing, and highlights the need and challenges for protecting third party users' privacy on crowdsourcing platforms. We have responsibly disclosed our findings to the related crowdsourcing platform and app providers. 

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3. "I would not install an app with this label": Privacy Label Impact on Risk Perception and Willingness to Install iOS Apps

   Balas, David G; Ali, Mir Masood; Kanich, Chris; Aviv, Adam J (August 2024, USENIX)

   Starting December 2020, all new and updated iOS apps must display app-based privacy labels. As the first large-scale implementation of privacy nutrition labels in a real-world setting, we aim to understand how these labels affect perceptions of app behavior. Replicating the methodology of Emani-Naeini et al. [IEEE S&P '21] in the space of IoT privacy nutrition labels, we conducted an online study in January 2023 on Prolific with n=1,505 participants to investigate the impact of privacy labels on users' risk perception and willingness to install apps. We found that many privacy label attributes raise participants' risk perception and lower their willingness to install an app. For example, when the app privacy label indicates that financial info will be collected and linked to their identities, participants were 15 times more likely to report increased privacy and security risks associated with the app. Likewise, when a label shows that sensitive info will be collected and used for cross-app/website tracking, participants were 304 times more likely to report a decrease in their willingness to install. However, participants had difficulty understanding privacy label jargon such as diagnostics, identifiers, track and linked. We provide recommendations for enhancing privacy label transparency, the importance of label clarity and accuracy, and how labels can impact consumer choice when suitable alternative apps are available. 

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4. “I would not install an app with this label”: Privacy Label Impact on Risk Perception and Willingness to Install iOS Apps

   Balash, David G; Ali, Mir Masood; Kanich, Chris; Aviv, Adam J (August 2024, USENIX)

   Starting December 2020, all new and updated iOS apps must display app-based privacy labels. As the first large-scale implementation of privacy nutrition labels in a real-world setting, we aim to understand how these labels affect perceptions of app behavior. Replicating the methodology of Emani-Naeini et al. (IEEE S&P '21) in the space of IoT privacy nutrition labels, we conducted an online study in January 2023 on Prolific with n=1,505 participants to investigate the impact of privacy labels on users' risk perception and willingness to install apps. We found that many privacy label attributes raise participants' risk perception and lower their willingness to install an app. For example, when the app privacy label indicates that \emph{financial info} will be collected and linked to their identities, participants were 15 times more likely to report increased privacy and security risks associated with the app. Likewise, when a label shows that \emph{sensitive info} will be collected and used for cross-app/website tracking, participants were 304 times more likely to report a decrease in their willingness to install. However, participants had difficulty understanding privacy label jargon such as "diagnostics," "identifiers," "track" and "linked." We provide recommendations for enhancing privacy label transparency, the importance of label clarity and accuracy, and how labels can impact consumer choice when suitable alternative apps are available. 

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5. Towards a Practical Differentially Private Collaborative Phone Blacklisting System

   https://doi.org/10.1145/3427228.3427239  

   Ucci, Daniele; Perdisci, Roberto; Lee, Jaewoo; Ahamad, Mustaque (December 2020, ACSAC 2020: Annual Computer Security Applications Conference)

   null (Ed.)

   Spam phone calls have been rapidly growing from nuisance to an increasingly effective scam delivery tool. To counter this increasingly successful attack vector, a number of commercial smartphone apps that promise to block spam phone calls have appeared on app stores, and are now used by hundreds of thousands or even millions of users. However, following a business model similar to some online social network services, these apps often collect call records or other potentially sensitive information from users’ phones with little or no formal privacy guarantees. In this paper, we study whether it is possible to build a practical collaborative phone blacklisting system that makes use of local differential privacy (LDP) mechanisms to provide clear privacy guarantees. We analyze the challenges and trade-offs related to using LDP, evaluate our LDP-based system on real-world user-reported call records collected by the FTC, and show that it is possible to learn a phone blacklist using a reasonable overall privacy budget and at the same time preserve users’ privacy while maintaining utility for the learned blacklist. 

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