12,273 research outputs found
Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions
Technology has recently been recruited in the war against the ongoing obesity
crisis; however, the adoption of Health & Fitness applications for regular
exercise is a struggle. In this study, we present a unique demographically
representative dataset of 15k US residents that combines technology use logs
with surveys on moral views, human values, and emotional contagion. Combining
these data, we provide a holistic view of individuals to model their physical
exercise behavior. First, we show which values determine the adoption of Health
& Fitness mobile applications, finding that users who prioritize the value of
purity and de-emphasize values of conformity, hedonism, and security are more
likely to use such apps. Further, we achieve a weighted AUROC of .673 in
predicting whether individual exercises, and we also show that the application
usage data allows for substantially better classification performance (.608)
compared to using basic demographics (.513) or internet browsing data (.546).
We also find a strong link of exercise to respondent socioeconomic status, as
well as the value of happiness. Using these insights, we propose actionable
design guidelines for persuasive technologies targeting health behavior
modification
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
Learning Location from Shared Elevation Profiles in Fitness Apps: A Privacy Perspective
The extensive use of smartphones and wearable devices has facilitated many
useful applications. For example, with Global Positioning System (GPS)-equipped
smart and wearable devices, many applications can gather, process, and share
rich metadata, such as geolocation, trajectories, elevation, and time. For
example, fitness applications, such as Runkeeper and Strava, utilize the
information for activity tracking and have recently witnessed a boom in
popularity. Those fitness tracker applications have their own web platforms and
allow users to share activities on such platforms or even with other social
network platforms. To preserve the privacy of users while allowing sharing,
several of those platforms may allow users to disclose partial information,
such as the elevation profile for an activity, which supposedly would not leak
the location of the users. In this work, and as a cautionary tale, we create a
proof of concept where we examine the extent to which elevation profiles can be
used to predict the location of users. To tackle this problem, we devise three
plausible threat settings under which the city or borough of the targets can be
predicted. Those threat settings define the amount of information available to
the adversary to launch the prediction attacks. Establishing that simple
features of elevation profiles, e.g., spectral features, are insufficient, we
devise both natural language processing (NLP)-inspired text-like representation
and computer vision-inspired image-like representation of elevation profiles,
and we convert the problem at hand into text and image classification problem.
We use both traditional machine learning- and deep learning-based techniques
and achieve a prediction success rate ranging from 59.59\% to 99.80\%. The
findings are alarming, highlighting that sharing elevation information may have
significant location privacy risks.Comment: 16 pages, 12 figures, 10 tables; accepted for publication in IEEE
Transactions on Mobile Computing (October 2022). arXiv admin note:
substantial text overlap with arXiv:1910.0904
Fitness First or Safety First? Examining Adverse Consequences of Privacy Seals in the Event of a Data Breach.
Data breaches are increasing, and fitness trackers have proven to be an ideal target, as they collect highly sensitive personal health data and are not governed by strict security guidelines. Nevertheless, companies encourage their customers to share data with the fitness tracker using privacy seals, gaining their trust without ensuring security. Since companies cannot guarantee security, the question arises on how privacy seals work after not keeping the security promise. This study examines the possibilities to mitigate the consequences of data breaches in advance to maintain the continuance intention. Expectation-confirmation theory (ECT) and privacy assurance statements as a shaping of privacy seals are used to influence customer expectations regarding the data security of fitness trackers in the run-up to a data breach. Results show that the use of privacy assurance statements leads to high-security expectations, and failure to meet these has a negative impact on satisfaction and thus continuance intention
Preserving Differential Privacy in Convolutional Deep Belief Networks
The remarkable development of deep learning in medicine and healthcare domain
presents obvious privacy issues, when deep neural networks are built on users'
personal and highly sensitive data, e.g., clinical records, user profiles,
biomedical images, etc. However, only a few scientific studies on preserving
privacy in deep learning have been conducted. In this paper, we focus on
developing a private convolutional deep belief network (pCDBN), which
essentially is a convolutional deep belief network (CDBN) under differential
privacy. Our main idea of enforcing epsilon-differential privacy is to leverage
the functional mechanism to perturb the energy-based objective functions of
traditional CDBNs, rather than their results. One key contribution of this work
is that we propose the use of Chebyshev expansion to derive the approximate
polynomial representation of objective functions. Our theoretical analysis
shows that we can further derive the sensitivity and error bounds of the
approximate polynomial representation. As a result, preserving differential
privacy in CDBNs is feasible. We applied our model in a health social network,
i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for
human behavior prediction, human behavior classification, and handwriting digit
recognition tasks. Theoretical analysis and rigorous experimental evaluations
show that the pCDBN is highly effective. It significantly outperforms existing
solutions
Conceptualizing human resilience in the face of the global epidemiology of cyber attacks
Computer security is a complex global phenomenon where different populations interact, and the infection of one person creates risk for another. Given the dynamics and scope of cyber campaigns, studies of local resilience without reference to global populations are inadequate. In this paper we describe a set of minimal requirements for implementing a global epidemiological infrastructure to understand and respond to large-scale computer security outbreaks. We enumerate the relevant dimensions, the applicable measurement tools, and define a systematic approach to evaluate cyber security resilience. From the experience in conceptualizing and designing a cross-national coordinated phishing resilience evaluation we describe the cultural, logistic, and regulatory challenges to this proposed public health approach to global computer assault resilience. We conclude that mechanisms for systematic evaluations of global attacks and the resilience against those attacks exist. Coordinated global science is needed to address organised global ecrime
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
Smart devices with built-in sensors, computational capabilities, and network
connectivity have become increasingly pervasive. The crowds of smart devices
offer opportunities to collectively sense and perform computing tasks in an
unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine
learning framework for a crowd of smart devices, which can solve a wide range
of learning problems for crowdsensing data with differential privacy
guarantees. Crowd-ML endows a crowdsensing system with an ability to learn
classifiers or predictors online from crowdsensing data privately with minimal
computational overheads on devices and servers, suitable for a practical and
large-scale employment of the framework. We analyze the performance and the
scalability of Crowd-ML, and implement the system with off-the-shelf
smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML
with real and simulated experiments under various conditions
Expecting the Unexpected in Security Violations in Mobile Apps
personal data. This increased access and control may raise usersâ perception of heightened privacy leakage and security issues. This is especially the case if usersâ awareness and expectations of this external access and control is not accurately recognized through proper security declarations. This proposal thus attempts to put forth an investigation on the effect of mobile usersâ privacy expectation disconfirmation on their continued usage intention of mobile apps sourced from app distribution stores. Drawing upon the APCO framework, security awareness literature and the expectation-disconfirmation perspective, two key types of security awareness information are identified; namely access annotation and modification annotation. It is noted that these types of information can be emphasized in app distribution stores to reduce subsequent privacy expectation disconfirmation. Hence, this study plans to examine the downstream effect of privacy expectation disconfirmation on usersâ continued usage intention. To operationalize this research, a laboratory experiment will be conducted
Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning
The simultaneous charging of many electric vehicles (EVs) stresses the
distribution system and may cause grid instability in severe cases. The best
way to avoid this problem is by charging coordination. The idea is that the EVs
should report data (such as state-of-charge (SoC) of the battery) to run a
mechanism to prioritize the charging requests and select the EVs that should
charge during this time slot and defer other requests to future time slots.
However, EVs may lie and send false data to receive high charging priority
illegally. In this paper, we first study this attack to evaluate the gains of
the lying EVs and how their behavior impacts the honest EVs and the performance
of charging coordination mechanism. Our evaluations indicate that lying EVs
have a greater chance to get charged comparing to honest EVs and they degrade
the performance of the charging coordination mechanism. Then, an anomaly based
detector that is using deep neural networks (DNN) is devised to identify the
lying EVs. To do that, we first create an honest dataset for charging
coordination application using real driving traces and information revealed by
EV manufacturers, and then we also propose a number of attacks to create
malicious data. We trained and evaluated two models, which are the multi-layer
perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the
GRU detector gives better results. Our evaluations indicate that our detector
can detect lying EVs with high accuracy and low false positive rate
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