As peer-to-peer (P2P) marketplaces have grown rapidly, concerns related to trust, privacy, and safety (TPS) have also increased. While previous studies have explored these aspects in various P2P marketplaces, there has been limited research on Facebook Marketplace (FM), which is distinguished by dramatic growth and intricate entanglement with the Facebook social networking site (SNS). To address this knowledge gap, we conducted interviews with 42 FM users in the US and Canada, investigating TPS factors associated with trading decisions. We identified four categories of factors: pre-existing concerns, signals, interactions, and perceived benefits. We uncover the challenges arising from the interplay of these factors, offer design recommendations for SNS–based marketplaces like FM, and suggest directions for future research. Our study advances the understanding of decision-making processes in SNS–based marketplaces, informs future design improvements for such platforms, and ultimately contributes to a better user experience related to trust, privacy, and safety.

Numerous information-tracking solutions have been implemented worldwide to fight the COVID-19 pandemic. While prior work has heavily explored the factors affecting people’s willingness to adopt contact-tracing solutions, which inform people when they have been exposed to someone positive for COVID-19, numerous countries have implemented other information-tracking solutions that use more data and more sensitive data than these commonly studied contact-tracing apps. In this work, we build on existing work focused on contact-tracing apps to explore adoption and design considerations for six representative information-tracking solutions for COVID-19, which differ in their goals and in the types of information they collect. To do so, we conducted semi-structured interviews with 44 participants to investigate the factors that influence their willingness to adopt these solutions. We find four main categories of influences on participants’ willingness to adopt such solutions: individual benefits of the solution, societal benefits of the solution, functionality concern, and digital safety (e.g., security and privacy) concerns. Further, we enumerate the factors that inform participants’ evaluations of these categories. Based on our findings, we make recommendations for the future design of information-tracking solutions and discuss how different factors may balance against benefits in future crisis situations.

This paper systematizes and contextualizes the existing body of knowledge on on technology’s dual nature regarding sexual abuse: facilitator of it and assistant to its prevention, reporting, and restriction. By reviewing 224 research papers, we identified 10 characteristics of technology that facilitate sexual abuse: covertness, publicness, anonymity, evolution, boundlessness, reproducibility, accessibility, indispensability, malleability, and opaqueness. We also analyzed how technology assists victims and other stakeholders in coping with and responding to sexual abuse. Our research questions examined the challenges in using technology to address sexual abuse too. For instance, its use by victims can lead to revictimization. To address technology’s challenges, we offer recommendations and suggest new research directions. These findings about the dual nature of technology can inform research and development toward better support for victims of sexual abuse.

We conducted semi-structured interviews with 20 users of Canada's exposure-notification app, COVID Alert. We identified several types of users' mental models for the app. Participants' concerns were found to correlate with their level of understanding of the app. Compared to a centralized contact-tracing app, COVID Alert was favored for its more efficient notification delivery method, its higher privacy protection, and its optional level of cooperation. Based on our findings, we suggest decision-makers rethink the app's privacy-utility trade-off and improve its utility by giving users more control over their data. We also suggest technology companies build and maintain trust with the public. Further, we recommend increasing diagnosed users' motivation to notify the app and encouraging exposed users to follow the guidelines. Last, we provide design suggestions to help users with Unsound and Innocent mental models to better understand the app.

Implicit authentication (IA) has recently become a popular approach for providing physical security on smartphones. It relies on behavioral traits (e.g., gait patterns) for user identification, instead of biometric data or knowledge of a PIN. However, it is not yet known whether users can understand the semantics of this technology well enough to use it properly. We bridge this knowledge gap by evaluating how Android’s Smart Lock (SL), which is the first widely deployed IA solution on smartphones, is understood by its users. We conducted a qualitative user study (N=26) and an online survey (N=331). The results suggest that users often have difficulty understanding SL semantics, leaving them unable to judge when their phone would be (un)locked. We found that various aspects of SL, such as its capabilities and its authentication factors, are confusing for the users. We also found that depth of smartphone adoption is a significant antecedent of SL comprehension.

With the rapid adoption of smart speakers in people's homes, there is a corresponding increase in users' privacy and security concerns. In contrast to previous studies of users' concerns about smart speakers' divulging private information to their manufacturers, our study focused on investigating users' concerns with regard to housemates and external entities. We conducted semi-structured interviews with 26 participants living in 21 households. Our results suggest that users often have an inadequate understanding of what data their smart speakers makes available to all users and what is kept private. Although participants expressed different privacy concerns about their housemates and external entities, they adopted similar, yet suboptimal, risk management strategies. We provide recommendations for future speaker design to support more optimal coping with the perceived risks.

Many people share online accounts, even in situations where high privacy and security are expected. Naturally, the sharing of these accounts does not endure forever. This paper reports the privacy and security challenges that people experience when they stop online account sharing. We conducted semi-structured interviews with 25 participants who stopped sharing at least one online account in the 12 months preceding the study. Our results suggest that users experience cognitive and psychosocial burdens when ending account sharing. We offer suggestions for how to improve the design of online accounts to support users better when they end account sharing.

Implicit authentication (IA) on smartphones has gained a lot of attention from the research community over the past decade. IA leverages behavioral and contextual data to identify users without requiring explicit input, and thus can alleviate the burden of smartphone unlocking. The reported studies on users’ perception of IA have painted a very positive picture, showing that more than 60% of their respective participants are interested in adopting IA, should it become available on their devices. These studies, however, have all been done either in lab environments, or with low- to medium-fidelity prototypes, which limits their generalizability and ecological validity. Therefore, the question of “how would smartphone users perceive a commercialized IA scheme in a realistic setting?” remains unanswered. To bridge this knowledge gap, we report on the findings of our qualitative user study (N = 26) and our online survey (N = 343) to understand how Android users perceive Smart Lock (SL). SL is the first and currently only widely-deployed IA scheme for smartphones. We found that SL is not a widely adopted technology, even among those who have an SL-enabled phone and are aware of the existence of the feature. Conversely, we found unclear usefulness, and perceived lack of security, among others, to be major adoption barriers that caused the SL adoption rate to be as low as 13%. To provide a theoretical framework for explaining SL adoption, we propose an extended version of the technology acceptance model (TAM), called SL-TAM, which sheds light on the importance of factors such as perceived security and utility on SL adoption.

The problem of trust is one of the more prominent security issue in online communications. This paper critically analyses and discusses the issue of trust in computer mediated introduction (CMI) where individuals are introduced for the purpose of interacting offline. One of the most popular forms of CMI today is online dating. We evaluate and compare the attempts made to solve the problem of trust in various computer mediated communications. We further specifically analyze three online dating platforms, Match.com, Plenty of Fish, and Tinder, and compare how they attempt to establish trust between potential matches. We find that existing mechanisms are not sufficient to establish meaningful trust in online dating. While we propose some potential alternative mechanisms for establishing trust in CMIs, the key contribution of this work is to identify the security challenges that arise in computer mediated introductions as a previously unrecognized class of security problems.