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  • Dynamic Autoselection and Autotuning of Machine Learning Models for Cloud Network Analytics

    • Authors: Karn, R. R.;  Advisor: -;  Participants: Kudva, P.; Elfadel, I. A. M. (2019)

  • Cloud network monitoring data is dynamic and distributed. Signals to monitor the cloud can appear, disappear or change their importance and clarity over time. Machine learning (ML) models tuned to a given data set can therefore quickly become inadequate. A model might be highly accurate at one point in time but may lose its accuracy at a later time due to changes in input data and their features. Distributed learning with dynamic model selection is therefore often required. Under such selection, poorly performing models (although aggressively tuned for the prior data) are retired or put on standby while newor standby models are brought in. The well-known method of Ensemble ML (EML) may potentially be applied to improve the overall accuracy of a family of ML models. Unfortunately, EM...
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  • An Efficient Outsourced Privacy Preserving Machine Learning Scheme With Public Verifiability

    • Authors: Hassan, A.;  Advisor: -;  Participants: Hamza, R.; Yan, H.; Li, P. (2019)

  • Cloud computing has been widely applied in numerous applications for storage and data analytics tasks. However, cloud servers engaged through a third party cannot be fully trusted by multiple data users. Thus, security and privacy concerns become the main obstructions to use machine learning services, especially with multiple data providers. Additionally, some recent outsourcing machine learning schemes have been proposed in order to preserve the privacy of data providers. Yet, these schemes cannot satisfy the property of public veri ability. In this paper, we present an ef cient privacy-preserving machine learning scheme for multiple data providers. The proposed scheme allows all participants in the system model to publicly verify the correctness of the encrypted data. Furthermore,...