8,465 research outputs found
Survey and Systematization of Secure Device Pairing
Secure Device Pairing (SDP) schemes have been developed to facilitate secure
communications among smart devices, both personal mobile devices and Internet
of Things (IoT) devices. Comparison and assessment of SDP schemes is
troublesome, because each scheme makes different assumptions about out-of-band
channels and adversary models, and are driven by their particular use-cases. A
conceptual model that facilitates meaningful comparison among SDP schemes is
missing. We provide such a model. In this article, we survey and analyze a wide
range of SDP schemes that are described in the literature, including a number
that have been adopted as standards. A system model and consistent terminology
for SDP schemes are built on the foundation of this survey, which are then used
to classify existing SDP schemes into a taxonomy that, for the first time,
enables their meaningful comparison and analysis.The existing SDP schemes are
analyzed using this model, revealing common systemic security weaknesses among
the surveyed SDP schemes that should become priority areas for future SDP
research, such as improving the integration of privacy requirements into the
design of SDP schemes. Our results allow SDP scheme designers to create schemes
that are more easily comparable with one another, and to assist the prevention
of persisting the weaknesses common to the current generation of SDP schemes.Comment: 34 pages, 5 figures, 3 tables, accepted at IEEE Communications
Surveys & Tutorials 2017 (Volume: PP, Issue: 99
From Signal to Social : Steps Towards Pervasive Social Context
The widespread adoption of smartphones with advanced sensing, computing and data transfer capabilities has made scientific studies of human social behavior possible at a previously unprecedented scale. It has also allowed context-awareness to become a natural feature in many applications using features such as activity recognition and location information. However, one of the most important aspects of context remains largely untapped at scale, i.e. social interactions and social context. Social interaction sensing has been explored using smartphones and specialized hardware for research purposes within computational social science and ubiquitous computing, but several obstacles remain to make it usable in practice by applications at industrial scale. In this thesis, I explore methods of physical proximity sensing and extraction of social context information from user-generated data for the purpose of context-aware applications. Furthermore, I explore the application space made possible through these methods, especially in the class of use cases that are characterized by embodied social agency, through field studies and a case study.A major concern when collecting context information is the impact on user privacy. I have performed a user study in which I have surveyed the user attitudes towards the privacy implications of proximity sensing. Finally, I present results from quantitatively estimating the sensitivity of a simple type of context information, i.e. application usage, in terms of risk of user re-identification
IoT Maps : Charting the Internet of Things
Internet of Things (IoT) devices are becoming increasingly ubiquitous in our everyday environments. While the number of devices and the degree of connectivity is growing, it is striking that as a society we are increasingly unaware of the locations and purposes of such devices. Indeed, much of the IoT technology being deployed is invisible and does not communicate its presence or purpose to the inhabitants of the spaces within which it is deployed. In this paper, we explore the potential benefits and challenges of constructing IoT maps that record the location of IoT devices. To illustrate the need for such maps, we draw on our experiences from multiple deployments of IoT systems.Peer reviewe
Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective
Blind people have limited access to information about their surroundings,
which is important for ensuring one's safety, managing social interactions, and
identifying approaching pedestrians. With advances in computer vision, wearable
cameras can provide equitable access to such information. However, the
always-on nature of these assistive technologies poses privacy concerns for
parties that may get recorded. We explore this tension from both perspectives,
those of sighted passersby and blind users, taking into account camera
visibility, in-person versus remote experience, and extracted visual
information. We conduct two studies: an online survey with MTurkers (N=206) and
an in-person experience study between pairs of blind (N=10) and sighted (N=40)
participants, where blind participants wear a working prototype for pedestrian
detection and pass by sighted participants. Our results suggest that both of
the perspectives of users and bystanders and the several factors mentioned
above need to be carefully considered to mitigate potential social tensions.Comment: The 2020 ACM CHI Conference on Human Factors in Computing Systems
(CHI 2020
Challenges in passenger use of mixed reality headsets in cars and other transportation
This paper examines key challenges in supporting passenger use of augmented and virtual reality headsets in transit. These headsets will allow passengers to break free from the restraints of physical displays placed in constrained environments such as cars, trains and planes. Moreover, they have the potential to allow passengers to make better use of their time by making travel more productive and enjoyable, supporting both privacy and immersion. However, there are significant barriers to headset usage by passengers in transit contexts. These barriers range from impediments that would entirely prevent safe usage and function (e.g. motion sickness) to those that might impair their adoption (e.g. social acceptability). We identify the key challenges that need to be overcome and discuss the necessary resolutions and research required to facilitate adoption and realize the potential advantages of using mixed reality headsets in transit
Context Data Categories and Privacy Model for Mobile Data Collection Apps
Context-aware applications stemming from diverse fields like mobile health,
recommender systems, and mobile commerce potentially benefit from knowing
aspects of the user's personality. As filling out personality questionnaires is
tedious, we propose the prediction of the user's personality from smartphone
sensor and usage data. In order to collect data for researching the
relationship between smartphone data and personality, we developed the Android
app TYDR (Track Your Daily Routine) which tracks smartphone data and utilizes
psychometric personality questionnaires. With TYDR, we track a larger variety
of smartphone data than similar existing apps, including metadata on
notifications, photos taken, and music played back by the user. For the
development of TYDR, we introduce a general context data model consisting of
four categories that focus on the user's different types of interactions with
the smartphone: physical conditions and activity, device status and usage, core
functions usage, and app usage. On top of this, we develop the privacy model
PM-MoDaC specifically for apps related to the collection of mobile data,
consisting of nine proposed privacy measures. We present the implementation of
all of those measures in TYDR. Although the utilization of the user's
personality based on the usage of his or her smartphone is a challenging
endeavor, it seems to be a promising approach for various types of
context-aware mobile applications.Comment: Accepted for publication at the 15th International Conference on
Mobile Systems and Pervasive Computing (MobiSPC 2018
How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage
As truly ubiquitous wearable computers, mobile phones are quickly becoming the primary source for social, behavioral and environmental sensing and data collection. Today’s smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and so on. In many cases, this analysis work is the result of exploratory forays and trial-and-error. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones for different sizes of analyzed networks. In particular, we examine how the ability to predict individual features and social links is incrementally enhanced with the accumulation of additional data. To accomplish this, we use the Friends and Family dataset, which contains rich data signals gathered from the smartphones of 130 adult members of a young-family residential community over the course of a year and consequently has become one of the most comprehensive mobile phone datasets gathered in academia to date. Our results show that features such as ethnicity, age and marital status can be detected by analyzing social and behavioral signals. We then investigate how the prediction accuracy is increased when the users sample set grows. Finally, we propose a method for advanced prediction of the maximal learning accuracy possible for the learning task at hand, based on an initial set of measurements. These predictions have practical implications, such as influencing the design of mobile data collection campaigns or evaluating analysis strategies
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