32,246 research outputs found
PinMe: Tracking a Smartphone User around the World
With the pervasive use of smartphones that sense, collect, and process
valuable information about the environment, ensuring location privacy has
become one of the most important concerns in the modern age. A few recent
research studies discuss the feasibility of processing data gathered by a
smartphone to locate the phone's owner, even when the user does not intend to
share his location information, e.g., when the Global Positioning System (GPS)
is off. Previous research efforts rely on at least one of the two following
fundamental requirements, which significantly limit the ability of the
adversary: (i) the attacker must accurately know either the user's initial
location or the set of routes through which the user travels and/or (ii) the
attacker must measure a set of features, e.g., the device's acceleration, for
potential routes in advance and construct a training dataset. In this paper, we
demonstrate that neither of the above-mentioned requirements is essential for
compromising the user's location privacy. We describe PinMe, a novel
user-location mechanism that exploits non-sensory/sensory data stored on the
smartphone, e.g., the environment's air pressure, along with publicly-available
auxiliary information, e.g., elevation maps, to estimate the user's location
when all location services, e.g., GPS, are turned off.Comment: This is the preprint version: the paper has been published in IEEE
Trans. Multi-Scale Computing Systems, DOI: 0.1109/TMSCS.2017.275146
Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications
Social Virtual Reality based Learning Environments (VRLEs) such as vSocial
render instructional content in a three-dimensional immersive computer
experience for training youth with learning impediments. There are limited
prior works that explored attack vulnerability in VR technology, and hence
there is a need for systematic frameworks to quantify risks corresponding to
security, privacy, and safety (SPS) threats. The SPS threats can adversely
impact the educational user experience and hinder delivery of VRLE content. In
this paper, we propose a novel risk assessment framework that utilizes attack
trees to calculate a risk score for varied VRLE threats with rate and duration
of threats as inputs. We compare the impact of a well-constructed attack tree
with an adhoc attack tree to study the trade-offs between overheads in managing
attack trees, and the cost of risk mitigation when vulnerabilities are
identified. We use a vSocial VRLE testbed in a case study to showcase the
effectiveness of our framework and demonstrate how a suitable attack tree
formalism can result in a more safer, privacy-preserving and secure VRLE
system.Comment: Tp appear in the CCNC 2019 Conferenc
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral
outbreak is the maintenance of the so-called Social Distancing (SD). To comply
with this constraint, workplaces, public institutions, transports and schools
will likely adopt restrictions over the minimum inter-personal distance between
people. Given this actual scenario, it is crucial to massively measure the
compliance to such physical constraint in our life, in order to figure out the
reasons of the possible breaks of such distance limitations, and understand if
this implies a possible threat given the scene context. All of this, complying
with privacy policies and making the measurement acceptable. To this end, we
introduce the Visual Social Distancing (VSD) problem, defined as the automatic
estimation of the inter-personal distance from an image, and the
characterization of the related people aggregations. VSD is pivotal for a
non-invasive analysis to whether people comply with the SD restriction, and to
provide statistics about the level of safety of specific areas whenever this
constraint is violated. We then discuss how VSD relates with previous
literature in Social Signal Processing and indicate which existing Computer
Vision methods can be used to manage such problem. We conclude with future
challenges related to the effectiveness of VSD systems, ethical implications
and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this
manuscript and they are listed by alphabetical order. Under submissio
Multiple imputation for sharing precise geographies in public use data
When releasing data to the public, data stewards are ethically and often
legally obligated to protect the confidentiality of data subjects' identities
and sensitive attributes. They also strive to release data that are informative
for a wide range of secondary analyses. Achieving both objectives is
particularly challenging when data stewards seek to release highly resolved
geographical information. We present an approach for protecting the
confidentiality of data with geographic identifiers based on multiple
imputation. The basic idea is to convert geography to latitude and longitude,
estimate a bivariate response model conditional on attributes, and simulate new
latitude and longitude values from these models. We illustrate the proposed
methods using data describing causes of death in Durham, North Carolina. In the
context of the application, we present a straightforward tool for generating
simulated geographies and attributes based on regression trees, and we present
methods for assessing disclosure risks with such simulated data.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS506 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Structuring the decision process : an evaluation of methods in the structuring the decision process
This chapter examines the effectiveness of methods that are designed to provide structure and support to decision making. Those that are primarily aimed at individual decision makers are examined first and then attention is turned to groups. In each case weaknesses of unaided decision making are identified and how successful the application of formal methods is likely to be in mitigating these weaknesses is assessed
Eliminating Latent Discrimination: Train Then Mask
How can we control for latent discrimination in predictive models? How can we
provably remove it? Such questions are at the heart of algorithmic fairness and
its impacts on society. In this paper, we define a new operational fairness
criteria, inspired by the well-understood notion of omitted variable-bias in
statistics and econometrics. Our notion of fairness effectively controls for
sensitive features and provides diagnostics for deviations from fair decision
making. We then establish analytical and algorithmic results about the
existence of a fair classifier in the context of supervised learning. Our
results readily imply a simple, but rather counter-intuitive, strategy for
eliminating latent discrimination. In order to prevent other features proxying
for sensitive features, we need to include sensitive features in the training
phase, but exclude them in the test/evaluation phase while controlling for
their effects. We evaluate the performance of our algorithm on several
real-world datasets and show how fairness for these datasets can be improved
with a very small loss in accuracy
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