Private and Scalable Personal Data Analytics Using Hybrid Edge-to-Cloud Deep Learning

Although the ability to collect, collate, and analyze the vast amount of data generated from cyber-physical systems and Internet of Things devices can be beneficial to both users and industry, this process has led to a number of challenges, including privacy and scalability issues. The authors present a hybrid framework where user-centered edge devices and resources can complement the cloud for providing privacy-aware, accurate, and efficient analytics.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Sandra Servia Rodríguez,et al.  Personal Model Training under Privacy Constraints , 2017, ArXiv.

[3]  Ian Goodfellow,et al.  Deep Learning with Differential Privacy , 2016, CCS.

[4]  Hamed Haddadi,et al.  A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics , 2017, IEEE Internet of Things Journal.

[5]  Thomas Brox,et al.  Inverting Visual Representations with Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Ian F. Akyildiz,et al.  A survey on wireless multimedia sensor networks , 2007, Comput. Networks.

[7]  Michael Naehrig,et al.  CryptoNets: applying neural networks to encrypted data with high throughput and accuracy , 2016, ICML 2016.

[8]  Cynthia Dwork,et al.  Differential Privacy: A Survey of Results , 2008, TAMC.

[9]  Luc Van Gool,et al.  DEX: Deep EXpectation of Apparent Age from a Single Image , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[10]  Vitaly Shmatikov,et al.  Privacy-preserving deep learning , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[11]  Hamed Haddadi,et al.  Personal Data: Thinking Inside the Box , 2015, Aarhus Conference on Critical Alternatives.

[12]  Philip S. Yu,et al.  A General Survey of Privacy-Preserving Data Mining Models and Algorithms , 2008, Privacy-Preserving Data Mining.

[13]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[14]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[15]  Yann LeCun,et al.  Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Narseo Vallina-Rodriguez,et al.  Breaking for commercials: characterizing mobile advertising , 2012, Internet Measurement Conference.