7,328 research outputs found
Are You in the Line? RSSI-based Queue Detection in Crowds
Crowd behaviour analytics focuses on behavioural characteristics of groups of
people instead of individuals' activities. This work considers human queuing
behaviour which is a specific crowd behavior of groups. We design a
plug-and-play system solution to the queue detection problem based on
Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs)
captured by multiple signal sniffers. The goal of this work is to determine if
a device is in the queue based on only RSSIs. The key idea is to extract
features not only from individual device's data but also mobility similarity
between data from multiple devices and mobility correlation observed by
multiple sniffers. Thus, we propose single-device feature extraction,
cross-device feature extraction, and cross-sniffer feature extraction for model
training and classification. We systematically conduct experiments with
simulated queue movements to study the detection accuracy. Finally, we compare
our signal-based approach against camera-based face detection approach in a
real-world social event with a real human queue. The experimental results
indicate that our approach can reach minimum accuracy of 77% and it
significantly outperforms the camera-based face detection because people block
each other's visibility whereas wireless signals can be detected without
blocking.Comment: This work has been partially funded by the European Union's Horizon
2020 research and innovation programme within the project "Worldwide
Interoperability for SEmantics IoT" under grant agreement Number 72315
Differentially Private Empirical Risk Minimization
Privacy-preserving machine learning algorithms are crucial for the
increasingly common setting in which personal data, such as medical or
financial records, are analyzed. We provide general techniques to produce
privacy-preserving approximations of classifiers learned via (regularized)
empirical risk minimization (ERM). These algorithms are private under the
-differential privacy definition due to Dwork et al. (2006). First we
apply the output perturbation ideas of Dwork et al. (2006), to ERM
classification. Then we propose a new method, objective perturbation, for
privacy-preserving machine learning algorithm design. This method entails
perturbing the objective function before optimizing over classifiers. If the
loss and regularizer satisfy certain convexity and differentiability criteria,
we prove theoretical results showing that our algorithms preserve privacy, and
provide generalization bounds for linear and nonlinear kernels. We further
present a privacy-preserving technique for tuning the parameters in general
machine learning algorithms, thereby providing end-to-end privacy guarantees
for the training process. We apply these results to produce privacy-preserving
analogues of regularized logistic regression and support vector machines. We
obtain encouraging results from evaluating their performance on real
demographic and benchmark data sets. Our results show that both theoretically
and empirically, objective perturbation is superior to the previous
state-of-the-art, output perturbation, in managing the inherent tradeoff
between privacy and learning performance.Comment: 40 pages, 7 figures, accepted to the Journal of Machine Learning
Researc
Privately Connecting Mobility to Infectious Diseases via Applied Cryptography
Human mobility is undisputedly one of the critical factors in infectious
disease dynamics. Until a few years ago, researchers had to rely on static data
to model human mobility, which was then combined with a transmission model of a
particular disease resulting in an epidemiological model. Recent works have
consistently been showing that substituting the static mobility data with
mobile phone data leads to significantly more accurate models. While prior
studies have exclusively relied on a mobile network operator's subscribers'
aggregated data, it may be preferable to contemplate aggregated mobility data
of infected individuals only. Clearly, naively linking mobile phone data with
infected individuals would massively intrude privacy. This research aims to
develop a solution that reports the aggregated mobile phone location data of
infected individuals while still maintaining compliance with privacy
expectations. To achieve privacy, we use homomorphic encryption, zero-knowledge
proof techniques, and differential privacy. Our protocol's open-source
implementation can process eight million subscribers in one and a half hours.
Additionally, we provide a legal analysis of our solution with regards to the
EU General Data Protection Regulation.Comment: Added differentlial privacy experiments and new benchmark
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