Differentially Private Tensor Train Deep Computation for Internet of Multimedia Things

Nicholaus J. Gati, Laurence Tianruo Yang, Jun Feng, Yijun Mo, Mamoun Alazab

    Research output: Contribution to journalArticlepeer-review


    The significant growth of the Internet of Things (IoT) takes a key and active role in healthcare, smart homes, smart manufacturing, and wearable gadgets. Due to complexness and difficulty in processing multimedia data, the IoT based scheme, namely Internet of Multimedia Things (IoMT) exists that is specialized for services and applications based on multimedia data. However, IoMT generated data are facing major processing and privacy issues. Therefore, tensor-based deep computation models proved a better platform to process IoMT generated data. A differentially private deep computation method working in the tensor space can attest to its efficacy for IoMT. Nevertheless, the deep computation model comprises a multitude of parameters; thus, it requires large units of memory and expensive computing units with higher performance levels, which hinders its performance for IoMT. Motivated by this, therefore, the paper proposes a deep private tensor train autoencoder (dPTTAE) technique to deal with IoMT generated data. Notably, the compression of weight tensors to manageable tensor train format is achieved through Tensor Train (TT) network. Moreover, TT format parameters are trained through higher-order back-propagation and gradient descent. We applied dPTTAE on three representative datasets. Comprehensive experimental evaluations and theoretical analysis show that dPTTAE enhances training time efficiency, and greatly improve memory utilization efficiency, attesting its potential for IoMT.

    Original languageEnglish
    Article number95
    Pages (from-to)1-20
    Number of pages20
    JournalACM Transactions on Multimedia Computing, Communications and Applications
    Issue number3s
    Publication statusPublished - Jan 2021


    Dive into the research topics of 'Differentially Private Tensor Train Deep Computation for Internet of Multimedia Things'. Together they form a unique fingerprint.

    Cite this