Privacy-Preserving Double-Projection Deep Computation Model With Crowdsourcing on Cloud for Big Data Feature Learning

Recent years have witness a considerable advance of Internet of Things with the tremendous progress of communication theories and sensing technologies. A large number of data, usually referring to big data, have been generated from Internet of Things. In this paper, we present a double-projection deep computation model (DPDCM) for big data feature learning, which projects the raw input into two separate subspaces in the hidden layers to learn interacted features of big data by replacing the hidden layers of the conventional deep computation model (DCM) with double-projection layers. Furthermore, we devise a learning algorithm to train the DPDCM. Cloud computing is used to improve the training efficiency of the learning algorithm by crowdsourcing the data on cloud. To protect the private data, a privacy-preserving DPDCM (PPDPDCM) is proposed based on the BGV encryption scheme. Finally, experiments are carried on Animal-20 and NUS-WIDE-14 to estimate the performance of DPDCM and PPDPDCM by comparing with DCM. Results demonstrate that DPDCM achieves a higher classification accuracy than DCM. More importantly, PPDPDCM can effectively improve the efficiency for training parameters, proving its potential for big data feature learning.

[1]  Qiyin Fang,et al.  Toward a Miniaturized Wireless Fluorescence-Based Diagnostic Imaging System , 2008, IEEE Journal of Selected Topics in Quantum Electronics.

[2]  M. Meyyappan,et al.  U-Health Smart Home , 2011, IEEE Nanotechnology Magazine.

[3]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[4]  M. Deen,et al.  A wireless wearable ECG sensor for long-term applications , 2012, IEEE Communications Magazine.

[5]  Craig Gentry,et al.  (Leveled) fully homomorphic encryption without bootstrapping , 2012, ITCS '12.

[6]  Nitish Srivastava,et al.  Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..

[7]  Zhikui Chen,et al.  A Universal Storage Architecture for Big Data in Cloud Environment , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.

[8]  Jianfeng Ma,et al.  Personal Health Records Integrity Verification Using Attribute Based Proxy Signature in Cloud Computing , 2013, IDCS.

[9]  Dong Yu,et al.  The Deep Tensor Neural Network With Applications to Large Vocabulary Speech Recognition , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Fei Wang,et al.  An Optimized Computational Model for Multi-Community-Cloud Social Collaboration , 2014, IEEE Transactions on Services Computing.

[11]  Zhikui Chen,et al.  A Distributed Weighted Possibilistic c-Means Algorithm for Clustering Incomplete Big Sensor Data , 2014, Int. J. Distributed Sens. Networks.

[12]  Laurence T. Yang,et al.  MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks , 2014, IEEE Transactions on Parallel and Distributed Systems.

[13]  Xue-wen Chen,et al.  Big Data Deep Learning: Challenges and Perspectives , 2014, IEEE Access.

[14]  Jing Gao,et al.  Composite event coverage in wireless sensor networks with heterogeneous sensors , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[15]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[16]  Laurence T. Yang,et al.  A nodes scheduling model based on Markov chain prediction for big streaming data analysis , 2015, Int. J. Commun. Syst..

[17]  Zhikui Chen,et al.  Distributed fuzzy c-means algorithms for big sensor data based on cloud computing , 2015, Int. J. Sens. Networks.

[18]  M. Jamal Deen,et al.  Information and communications technologies for elderly ubiquitous healthcare in a smart home , 2015, Personal and Ubiquitous Computing.

[19]  Jiantao Zhou,et al.  Support-Set-Assured Parallel Outsourcing of Sparse Reconstruction Service for Compressive Sensing in Multi-clouds , 2015, 2015 International Symposium on Security and Privacy in Social Networks and Big Data (SocialSec).

[20]  Changsheng Xu,et al.  Cross-Domain Feature Learning in Multimedia , 2015, IEEE Transactions on Multimedia.

[21]  Gang Zhou,et al.  Determining driver phone use leveraging smartphone sensors , 2015, Multimedia Tools and Applications.

[22]  Jie Wu,et al.  Energy Efficiency and Contact Opportunities Tradeoff in Opportunistic Mobile Networks , 2016, IEEE Transactions on Vehicular Technology.

[23]  Laurence T. Yang,et al.  Deep Computation Model for Unsupervised Feature Learning on Big Data , 2016, IEEE Transactions on Services Computing.

[24]  Laurence T. Yang,et al.  Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning , 2016, IEEE Transactions on Computers.

[25]  Minyi Guo,et al.  Mobile Crowdsensing in Software Defined Opportunistic Networks , 2017, IEEE Communications Magazine.

[26]  Laurence T. Yang,et al.  An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things , 2017, IEEE Transactions on Industrial Informatics.

[27]  Laurence T. Yang,et al.  A Tucker Deep Computation Model for Mobile Multimedia Feature Learning , 2017, ACM Trans. Multim. Comput. Commun. Appl..

[28]  Laurence T. Yang,et al.  A privacy-preserving high-order neuro-fuzzy c-means algorithm with cloud computing , 2017, Neurocomputing.

[29]  Laurence T. Yang,et al.  High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT , 2018, Inf. Fusion.

[30]  Yueming Hu,et al.  Distributed Feature Selection for Efficient Economic Big Data Analysis , 2018, IEEE Transactions on Big Data.

[31]  Athanasios V. Vasilakos,et al.  Multimedia Processing Pricing Strategy in GPU-Accelerated Cloud Computing , 2020, IEEE Transactions on Cloud Computing.