Adaptive Blurring of Sensor Data to balance Privacy and Utility for Ubiquitous Services

Given the trend towards mobile computing, the next generation
of ubiquitous “smart” services will have to continuously
analyze surrounding sensor data. More than ever, such
services will rely on data potentially related to personal activities
to perform their tasks, e.g. to predict urban traffic
or local weather conditions. However, revealing personal
data inevitably entails privacy risks, especially when data is
shared with high precision and frequency. For example, by
analyzing the precise electric consumption data, it can be inferred
if a person is currently at home, however this can empower
new services such as a smart heating system. Access
control (forbid or grant access) or anonymization techniques
are not able to deal with such trade-off because whether
they completely prohibit access to data or lose source traceability.
Blurring techniques, by tuning data quality, offer
a wide range of trade-offs between privacy and utility for
services. However, the amount of ubiquitous services and
their data quality requirements lead to an explosion of possible
configurations of blurring algorithms. To manage this
complexity, in this paper we propose a platform that automatically
adapts (at runtime) blurring components between
data owners and data consumers (services). The platform
searches the optimal trade-off between service utility and
privacy risks using multi-objective evolutionary algorithms
to adapt the underlying communication platform. We evaluate
our approach on a sensor network gateway and show its
suitability in terms of i) effectiveness to find an appropriate
solution, ii) efficiency and scalability.