  This paper introduces PriMaL, a general PRIvacy-preserving MAchine-Learning
method for reducing the privacy cost of information transmitted through a
network. Distributed sensor networks are often used for automated
classification and detection of abnormal events in high-stakes situations, e.g.
fire in buildings, earthquakes, or crowd disasters. Such networks might
transmit privacy-sensitive information, e.g. GPS location of smartphones, which
might be disclosed if the network is compromised. Privacy concerns might slow
down the adoption of the technology, in particular in the scenario of social
sensing where participation is voluntary, thus solutions are needed which
improve privacy without compromising on the event detection accuracy. PriMaL is
implemented as a machine-learning layer that works on top of an existing event
detection algorithm. Experiments are run in a general simulation framework, for
several network topologies and parameter values. The privacy footprint of
state-of-the-art event detection algorithms is compared within the proposed
framework. Results show that PriMaL is able to reduce the privacy cost of a
distributed event detection algorithm below that of the corresponding
centralized algorithm, within the bounds of some assumptions about the
protocol. Moreover the performance of the distributed algorithm is not
statistically worse than that of the centralized algorithm.
