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

Multi-class Classifier based Failure Prediction with Artificial and Anonymous Training for Data Privacy