An official website of the United States government
In recent years, we have witnessed a rise in the popularity of net- worked hospitality services (NHSs), an online marketplace for short-term peer- to-peer accommodations. Such systems, however, raise significant privacy con- cerns, because service providers such as Airbnb and 9flats can easily collect the precise and personal information of millions of participating hosts and guests through their centralized online platforms. In this paper, we propose PrivateNH, a privacy-enhancing and practical solution that offers anonymity and accountabil- ity for NHS users without relying on any trusted third party. PrivateNH leverages the recent progress of Bitcoin techniques such as Colored Coins and CoinShuffle to generate and maintain anonymous credentials for NHS participants. The cre- dential holders (NHS hosts or guests) can then lease or rent short-term lodging and interact with the service provider in an anonymous and accountable man- ner. An anonymous and secure reputation system is also introduced to establish the trust between unfamiliar hosts and guests in a peer-to-peer fashion. The pro- posed scheme is compatible with the current Bitcoin blockchain system, and its effectiveness and feasibility in NHS scenario are also demonstrated by security analysis and performance evaluation.
more »
« less
Well-intended but half-hearted: Hosts’ consideration of guests’ privacy using smart devices on rental properties
The increased use of smart home devices (SHDs) on short- term rental (STR) properties raises privacy concerns for guests. While previous literature identifies guests’ privacy concerns and the need to negotiate guests’ privacy prefer- ences with hosts, there is a lack of research from the hosts’ perspectives. This paper investigates if and how hosts con- sider guests’ privacy when using their SHDs on their STRs, to understand hosts’ willingness to accommodate guests’ pri- vacy concerns, a starting point for negotiation. We conducted online interviews with 15 STR hosts (e.g., Airbnb/Vrbo), find- ing that they generally use, manage, and disclose their SHDs in ways that protect guests’ privacy. However, hosts’ prac- tices fell short of their intentions because of competing needs and goals (i.e., protecting their property versus protecting guests’ privacy). Findings also highlight that hosts do not have proper support from the platforms on how to navigate these competing goals. Therefore, we discuss how to improve platforms’ guidelines/policies to prevent and resolve conflicts with guests and measures to increase engagement from both sides to set ground for negotiation.
more »
« less
- Award ID(s):
- 1955805
- PAR ID:
- 10618339
- Publisher / Repository:
- USENIX
- Date Published:
- ISBN:
- 978-1-939133-42-7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
Abstract One of the major challenges in ensuring global food security is the ever‐changing biotic risk affecting the productivity and efficiency of the global food supply system. Biotic risks that threaten food security include pests and diseases that affect pre‐ and postharvest terrestrial agriculture and aquaculture. Strategies to minimize this risk depend heavily on plant and animal disease research. As data collected at high spatial and temporal resolutions become increasingly available, epidemiological models used to assess and predict biotic risks have become more accurate and, thus, more useful. However, with the advent of Big Data opportunities, a number of challenges have arisen that limit researchers’ access to complex, multi‐sourced, multi‐scaled data collected on pathogens, and their associated environments and hosts. Among these challenges, one of the most limiting factors is data privacy concerns from data owners and collectors. While solutions, such as the use of de‐identifying and anonymizing tools that protect sensitive information are recognized as effective practices for use by plant and animal disease researchers, there are comparatively few platforms that include data privacy by design that are accessible to researchers. We describe how the general thinking and design used for data sharing and analysis platforms can intrinsically address a number of these data privacy‐related challenges that are a barrier to researchers wanting to access data. We also describe how some of the data privacy concerns confronting plant and animal disease researchers are addressed by way of the GEMS informatics platform.more » « less
-
Hornbæk, Kasper (Ed.)Smart home devices, such as security cameras and voice assistants, have seen widespread adoption due to the utility and convenience they offer to users. The deployment of these devices in homes, however, raises privacy concerns for bystanders—people who may not necessarily have a say in the deployment and configuration of these devices, and yet are exposed to or affected by their data collection. Examples of bystanders include guests, short-term tenants, and domestic workers. Prior work has studied the privacy concerns of different bystander groups and proposed design solutions for addressing these concerns. In this article, we present a systematic review of previous studies, describing how smart home bystanders are defined and classified, and illuminating the range of concerns and solutions proposed in the existing academic literature. We also discuss limitations in prior work, barriers to the uptake of research-based solutions by industry, and identify avenues for future research.more » « less
-
User reporting is an essential component of content moderation on many online platforms--in particular, on end-to-end encrypted (E2EE) messaging platforms where platform operators cannot proactively inspect message contents. However, users' privacy concerns when considering reporting may impede the effectiveness of this strategy in regulating online harassment. In this paper, we conduct interviews with 16 users of E2EE platforms to understand users' mental models of how reporting works and their resultant privacy concerns and considerations surrounding reporting. We find that users expect platforms to store rich longitudinal reporting datasets, recognizing both their promise for better abuse mitigation and the privacy risk that platforms may exploit or fail to protect them. We also find that users have preconceptions about the respective capabilities and risks of moderators at the platform versus community level--for instance, users trust platform moderators more to not abuse their power but think community moderators have more time to attend to reports. These considerations, along with perceived effectiveness of reporting and how to provide sufficient evidence while maintaining privacy, shape how users decide whether, to whom, and how much to report. We conclude with design implications for a more privacy-preserving reporting system on E2EE messaging platforms.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
