45,311 research outputs found
XYZ Privacy
Future autonomous vehicles will generate, collect, aggregate and consume
significant volumes of data as key gateway devices in emerging Internet of
Things scenarios. While vehicles are widely accepted as one of the most
challenging mobility contexts in which to achieve effective data
communications, less attention has been paid to the privacy of data emerging
from these vehicles. The quality and usability of such privatized data will lie
at the heart of future safe and efficient transportation solutions.
In this paper, we present the XYZ Privacy mechanism. XYZ Privacy is to our
knowledge the first such mechanism that enables data creators to submit
multiple contradictory responses to a query, whilst preserving utility measured
as the absolute error from the actual original data. The functionalities are
achieved in both a scalable and secure fashion. For instance, individual
location data can be obfuscated while preserving utility, thereby enabling the
scheme to transparently integrate with existing systems (e.g. Waze). A new
cryptographic primitive Function Secret Sharing is used to achieve
non-attributable writes and we show an order of magnitude improvement from the
default implementation.Comment: arXiv admin note: text overlap with arXiv:1708.0188
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems
Our everyday interactions with pervasive systems generate traces that capture
various aspects of human behavior and enable machine learning algorithms to
extract latent information about users. In this paper, we propose a machine
learning interpretability framework that enables users to understand how these
generated traces violate their privacy
Preserving Co-Location Privacy in Geo-Social Networks
The number of people on social networks has grown exponentially. Users share
very large volumes of personal informations and content every days. This
content could be tagged with geo-spatial and temporal coordinates that may be
considered sensitive for some users. While there is clearly a demand for users
to share this information with each other, there is also substantial demand for
greater control over the conditions under which their information is shared.
Content published in a geo-aware social networks (GeoSN) often involves
multiple users and it is often accessible to multiple users, without the
publisher being aware of the privacy preferences of those users. This makes
difficult for GeoSN users to control which information about them is available
and to whom it is available. Thus, the lack of means to protect users privacy
scares people bothered about privacy issues. This paper addresses a particular
privacy threats that occur in GeoSNs: the Co-location privacy threat. It
concerns the availability of information about the presence of multiple users
in a same locations at given times, against their will. The challenge addressed
is that of supporting privacy while still enabling useful services.Comment: 10 pages, 5 figure
- …