62 research outputs found
Towards a Practical Pedestrian Distraction Detection Framework using Wearables
Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices
Exploiting Out-of-band Motion Sensor Data to De-anonymize Virtual Reality Users
Virtual Reality (VR) is an exciting new consumer technology which offers an
immersive audio-visual experience to users through which they can navigate and
interact with a digitally represented 3D space (i.e., a virtual world) using a
headset device. By (visually) transporting users from the real or physical
world to exciting and realistic virtual spaces, VR systems can enable
true-to-life and more interactive versions of traditional applications such as
gaming, remote conferencing, social networking and virtual tourism. However, as
with any new consumer technology, VR applications also present significant
user-privacy challenges. This paper studies a new type of privacy attack
targeting VR users by connecting their activities visible in the virtual world
(enabled by some VR application/service) to their physical state sensed in the
real world. Specifically, this paper analyzes the feasibility of carrying out a
de-anonymization or identification attack on VR users by correlating visually
observed movements of users' avatars in the virtual world with some auxiliary
data (e.g., motion sensor data from mobile/wearable devices held by users)
representing their context/state in the physical world. To enable this attack,
this paper proposes a novel framework which first employs a learning-based
activity classification approach to translate the disparate visual movement
data and motion sensor data into an activity-vector to ease comparison,
followed by a filtering and identity ranking phase outputting an ordered list
of potential identities corresponding to the target visual movement data.
Extensive empirical evaluation of the proposed framework, under a comprehensive
set of experimental settings, demonstrates the feasibility of such a
de-anonymization attack
BayBFed: Bayesian Backdoor Defense for Federated Learning
Federated learning (FL) allows participants to jointly train a machine
learning model without sharing their private data with others. However, FL is
vulnerable to poisoning attacks such as backdoor attacks. Consequently, a
variety of defenses have recently been proposed, which have primarily utilized
intermediary states of the global model (i.e., logits) or distance of the local
models (i.e., L2-norm) from the global model to detect malicious backdoors.
However, as these approaches directly operate on client updates, their
effectiveness depends on factors such as clients' data distribution or the
adversary's attack strategies. In this paper, we introduce a novel and more
generic backdoor defense framework, called BayBFed, which proposes to utilize
probability distributions over client updates to detect malicious updates in
FL: it computes a probabilistic measure over the clients' updates to keep track
of any adjustments made in the updates, and uses a novel detection algorithm
that can leverage this probabilistic measure to efficiently detect and filter
out malicious updates. Thus, it overcomes the shortcomings of previous
approaches that arise due to the direct usage of client updates; as our
probabilistic measure will include all aspects of the local client training
strategies. BayBFed utilizes two Bayesian Non-Parametric extensions: (i) a
Hierarchical Beta-Bernoulli process to draw a probabilistic measure given the
clients' updates, and (ii) an adaptation of the Chinese Restaurant Process
(CRP), referred by us as CRP-Jensen, which leverages this probabilistic measure
to detect and filter out malicious updates. We extensively evaluate our defense
approach on five benchmark datasets: CIFAR10, Reddit, IoT intrusion detection,
MNIST, and FMNIST, and show that it can effectively detect and eliminate
malicious updates in FL without deteriorating the benign performance of the
global model
Optimizing Mix-zone Coverage in Pervasive Wireless Networks
Location privacy is a major concern in pervasive networks where static device identifiers enable malicious eavesdroppers to continuously track users and their movements. In order to prevent such identifier-based tracking, devices could coordinate regular identifier change operations in special areas called mix-zones. Although mix-zones provide spatio-temporal de-correlation between old and new identifiers, depending on the position of the mix-zone, identifier changes can generate a substantial inconvenience (or ``cost") to the users in terms of lost communications and increased energy consumption. In this paper, we address this trade-off between privacy and cost by studying the problem of determining an optimal set of mix-zones such that the degree of mixing in the network is maximized and the overall network-wide mixing cost is minimized. We follow a graph-theoretic approach and model the optimal mixing problem as a novel generalization of the vertex cover problem, called the \textit{Mix Cover (MC)} problem. We propose three approximation algorithms for the MC problem and derive a lower bound on the solution quality guaranteed by them. We also outline two other heuristics for solving the MC problem, which are simple but do not provide any guarantees on the solution quality. By means of extensive empirical evaluation using real data, we compare the performance and solution quality of these algorithms. The combinatorics-based approach used in this work enables us to study the feasibility of determining optimal mix-zones regularly and under dynamic network conditions
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