12,406 research outputs found
Measuring Membership Privacy on Aggregate Location Time-Series
While location data is extremely valuable for various applications,
disclosing it prompts serious threats to individuals' privacy. To limit such
concerns, organizations often provide analysts with aggregate time-series that
indicate, e.g., how many people are in a location at a time interval, rather
than raw individual traces. In this paper, we perform a measurement study to
understand Membership Inference Attacks (MIAs) on aggregate location
time-series, where an adversary tries to infer whether a specific user
contributed to the aggregates.
We find that the volume of contributed data, as well as the regularity and
particularity of users' mobility patterns, play a crucial role in the attack's
success. We experiment with a wide range of defenses based on generalization,
hiding, and perturbation, and evaluate their ability to thwart the attack
vis-a-vis the utility loss they introduce for various mobility analytics tasks.
Our results show that some defenses fail across the board, while others work
for specific tasks on aggregate location time-series. For instance, suppressing
small counts can be used for ranking hotspots, data generalization for
forecasting traffic, hotspot discovery, and map inference, while sampling is
effective for location labeling and anomaly detection when the dataset is
sparse. Differentially private techniques provide reasonable accuracy only in
very specific settings, e.g., discovering hotspots and forecasting their
traffic, and more so when using weaker privacy notions like crowd-blending
privacy. Overall, our measurements show that there does not exist a unique
generic defense that can preserve the utility of the analytics for arbitrary
applications, and provide useful insights regarding the disclosure of sanitized
aggregate location time-series
Continuous measurements of real-life bidirectional pedestrian flows on a wide walkway
Employing partially overlapping overhead \kinectTMS sensors and automatic
pedestrian tracking algorithms we recorded the crowd traffic in a rectilinear
section of the main walkway of Eindhoven train station on a 24/7 basis. Beside
giving access to the train platforms (it passes underneath the railways), the
walkway plays an important connection role in the city. Several crowding
scenarios occur during the day, including high- and low-density dynamics in
uni- and bi-directional regimes. In this paper we discuss our recording
technique and we illustrate preliminary data analyses. Via fundamental
diagrams-like representations we report pedestrian velocities and fluxes vs.
pedestrian density. Considering the density range - ped/m, we
find that at densities lower than ped/m pedestrians in
unidirectional flows walk faster than in bidirectional regimes. On the
opposite, velocities and fluxes for even bidirectional flows are higher above
ped/m.Comment: 9 pages, 7 figure
Enter the Circle: Blending Spherical Displays and Playful Embedded Interaction in Public Spaces
Public displays are used a variety of contexts, from utility
driven information displays to playful entertainment displays.
Spherical displays offer new opportunities for interaction
in public spaces, allowing users to face each other
during interaction and explore content from a variety of
angles and perspectives. This paper presents a playful installation
that places a spherical display at the centre of a
playful environment embedded with interactive elements.
The installation, called Enter the Circle, involves eight
chair-sized boxes filled with interactive lights that can be
controlled by touching the spherical display. The boxes are
placed in a ring around the display, and passers-by must
“enter the circle” to explore and play with the installation.
We evaluated this installation in a pedestrianized walkway
for three hours over an evening, collecting on-screen logs
and video data. This paper presents a novel evaluation of a
spherical display in a public space, discusses an experimental
design concept that blends displays with embedded
interaction, and analyses real world interaction with the
installation
Prochlo: Strong Privacy for Analytics in the Crowd
The large-scale monitoring of computer users' software activities has become
commonplace, e.g., for application telemetry, error reporting, or demographic
profiling. This paper describes a principled systems architecture---Encode,
Shuffle, Analyze (ESA)---for performing such monitoring with high utility while
also protecting user privacy. The ESA design, and its Prochlo implementation,
are informed by our practical experiences with an existing, large deployment of
privacy-preserving software monitoring.
(cont.; see the paper
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