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.

Abstract As the smartphone market leader, Android has been a prominent target for malware attacks. The number of malicious applications (apps) identified for it has increased continually over the past decade, creating an immense challenge for all parties involved. For market holders and researchers, in particular, the large number of samples has made manual malware detection unfeasible, leading to an influx of research that investigate Machine Learning (ML) approaches to automate this process. However, while some of the proposed approaches achieve high performance, rapidly evolving Android malware has made them unable to maintain their accuracy over time. This has created a need in the community to conduct further research, and build more flexible ML pipelines. Doing so, however, is currently hindered by a lack of systematic overview of the existing literature, to learn from and improve upon the existing solutions. Existing survey papers often focus only on parts of the ML process (e.g., data collection or model deployment), while omitting other important stages, such as model evaluation and explanation. In this paper, we address this problem with a review of 42 highly-cited papers, spanning a decade of research (from 2011 to 2021). We introduce a novel procedural taxonomy of the published literature, covering how they have used ML algorithms, what features they have engineered, which dimensionality reduction techniques they have employed, what datasets they have employed for training, and what their evaluation and explanation strategies are. Drawing from this taxonomy, we also identify gaps in knowledge and provide ideas for improvement and future work.

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.

In a corpus of 45,821 app reviews of the top five mobile cryptocurrency wallets, we identified and qualitatively analyzed 6,859 reviews pertaining to the user experience (UX) with those wallets. Our analysis suggests that both new and experienced users struggle with general and domain-specific UX issues that, aside from frustration and disengagement, might lead to dangerous errors and irreversible monetary losses. We reveal shortcomings of current wallet UX as well as users’ misconceptions, some of which can be traced back to a reliance on their understanding of conventional payment systems. For example, some users believed that transactions were free, reversible, and could be canceled anytime, which is not the case in reality. Correspondingly, these beliefs often resulted in unmet expectations. Based on our findings, we provide recommendations on how to design cryptocurrency wallets that both alleviate the identified issues and counteract some of the misconceptions in order to better support newcomers.

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.

Maximizing quality of aggregation (QoA) is an essential requirement for real-time wireless sensor networks (WSNs) where the participation of all sensor nodes in data aggregation is hampered by the underlying sink deadline and interference constraints. This problem, however, remains unsolved under the physical interference model that captures the reality more accurately than the widely used graph-based models. In this paper, we formulate an optimization problem of maximizing QoA under deadline and interference constraints in commonly seen tree- based WSNs. We prove the problem to be NP- complete, and then propose a suboptimal scheduling algorithm which relies on a Markov approximation framework and modifies the matching graphs in order to handle the globally-imposed interference constraints. The problem and its solution are then coupled with successive interference cancellation (SIC) to improve QoA by increasing the number of concurrent transmissions. Our evaluation has shown the proposed solution to be effective under the physical interference model, both with and without SIC.