12,186 research outputs found
The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps
Third party apps that work on top of personal cloud services such as Google
Drive and Dropbox, require access to the user's data in order to provide some
functionality. Through detailed analysis of a hundred popular Google Drive apps
from Google's Chrome store, we discover that the existing permission model is
quite often misused: around two thirds of analyzed apps are over-privileged,
i.e., they access more data than is needed for them to function. In this work,
we analyze three different permission models that aim to discourage users from
installing over-privileged apps. In experiments with 210 real users, we
discover that the most successful permission model is our novel ensemble method
that we call Far-reaching Insights. Far-reaching Insights inform the users
about the data-driven insights that apps can make about them (e.g., their
topics of interest, collaboration and activity patterns etc.) Thus, they seek
to bridge the gap between what third parties can actually know about users and
users perception of their privacy leakage. The efficacy of Far-reaching
Insights in bridging this gap is demonstrated by our results, as Far-reaching
Insights prove to be, on average, twice as effective as the current model in
discouraging users from installing over-privileged apps. In an effort for
promoting general privacy awareness, we deploy a publicly available privacy
oriented app store that uses Far-reaching Insights. Based on the knowledge
extracted from data of the store's users (over 115 gigabytes of Google Drive
data from 1440 users with 662 installed apps), we also delineate the ecosystem
for third-party cloud apps from the standpoint of developers and cloud
providers. Finally, we present several general recommendations that can guide
other future works in the area of privacy for the cloud
FogGIS: Fog Computing for Geospatial Big Data Analytics
Cloud Geographic Information Systems (GIS) has emerged as a tool for
analysis, processing and transmission of geospatial data. The Fog computing is
a paradigm where Fog devices help to increase throughput and reduce latency at
the edge of the client. This paper developed a Fog-based framework named Fog
GIS for mining analytics from geospatial data. We built a prototype using Intel
Edison, an embedded microprocessor. We validated the FogGIS by doing
preliminary analysis. including compression, and overlay analysis. Results
showed that Fog computing hold a great promise for analysis of geospatial data.
We used several open source compression techniques for reducing the
transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section
International Conference on Electrical, Computer and Electronics (09-11
December, 2016) Indian Institute of Technology (Banaras Hindu University)
Varanasi, Indi
- …