  This paper proposes a novel non-intrusive system failure prediction technique
using available information from developers and minimal information from raw
logs (rather than mining entire logs) but keeping the data entirely private
with the data owners. A neural network based multi-class classifier is
developed for failure prediction, using artificially generated anonymous data
set, applying a combination of techniques, viz., genetic algorithm (steps),
pattern repetition, etc., to train and test the network. The proposed mechanism
completely decouples the data set used for training process from the actual
data which is kept private. Moreover, multi-criteria decision making (MCDM)
schemes are used to prioritize failures meeting business requirements. Results
show high accuracy in failure prediction under different parameter
configurations. On a broader context, any classification problem, beyond
failure prediction, can be performed using the proposed mechanism with
artificially generated data set without looking into the actual data as long as
the input features can be translated to binary values (e.g. output from private
binary classifiers) and can provide classification-as-a-service.
