XYZ Privacy
File Type:
PDFItem Type:
OtherDate:
2018Access:
openAccessCitation:
Joy, J., Gray, D., Mc Goldrick, C. & Gerla, M. XYZ Privacy, 2018, 1-17Download Item:
Abstract:
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.
Author's Homepage:
http://people.tcd.ie/cmcgldrkType of material:
OtherCollections
Availability:
Full text availableKeywords:
Autonomous vehicles, Internet of Things, Data privacyMetadata
Show full item recordLicences: