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Differentially Private Tensor Train Deep Computation for Internet of Multimedia Things
ACM Transactions on Multimedia Computing, Communications, and Applications ( IF 5.2 ) Pub Date : 2020-12-31 , DOI: 10.1145/3421276
Nicholaus J. Gati 1 , Laurence T. Yang 2 , Jun Feng 1 , Yijun Mo 3 , Mamoun Alazab 3
Affiliation  

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.

中文翻译:

多媒体物联网的差分私有张量训练深度计算

物联网 (IoT) 的显着增长在医疗保健、智能家居、智能制造和可穿戴设备中发挥了关键和积极的作用。由于多媒体数据处理的复杂性和难度,存在专门针对基于多媒体数据的服务和应用的基于物联网的方案,即多媒体物联网(IoMT)。然而,物联网生成的数据面临着重大的处理和隐私问题。因此,基于张量的深度计算模型被证明是处理 IoMT 生成数据的更好平台。在张量空间中工作的差分私有深度计算方法可以证明其对 IoMT 的功效。然而,深度计算模型包含大量参数;因此,它需要具有更高性能水平的大容量内存和昂贵的计算单元,这阻碍了它在物联网中的表现。因此,受此启发,本文提出了一种深度私有张量训练自动编码器 (dPTTAE) 技术来处理 IoMT 生成的数据。值得注意的是,将权重张量压缩为可管理的张量训练格式是通过张量训练 (TT) 网络实现的。此外,TT 格式参数通过高阶反向传播和梯度下降进行训练。我们在三个有代表性的数据集上应用了 dPTTAE。综合实验评估和理论分析表明,dPTTAE 提高了训练时间效率,大大提高了内存利用效率,证明了其在 IoMT 方面的潜力。将权重张量压缩为可管理的张量训练格式是通过张量训练(TT)网络实现的。此外,TT 格式参数通过高阶反向传播和梯度下降进行训练。我们在三个有代表性的数据集上应用了 dPTTAE。综合实验评估和理论分析表明,dPTTAE 提高了训练时间效率,大大提高了内存利用效率,证明了其在 IoMT 方面的潜力。将权重张量压缩为可管理的张量训练格式是通过张量训练(TT)网络实现的。此外,TT 格式参数通过高阶反向传播和梯度下降进行训练。我们在三个有代表性的数据集上应用了 dPTTAE。综合实验评估和理论分析表明,dPTTAE 提高了训练时间效率,大大提高了内存利用效率,证明了其在 IoMT 方面的潜力。
更新日期:2020-12-31
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