Deep Neural Networks as Similitude Models for Sharing Big Data

Philip Derbeko, Shlomi Dolev, Ehud Gudes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The amount of data grows rapidly with time and shows no signs of stopping. Ubiquitous computing continues to collect and generate more and more data as both the number of devices grows and the capabilities of devices increase. We suggest processing the data on end devices by building a representative model of the data ('similitude' model). Sharing a smaller model instead of the entire data allows for saving computing power, network time, processing time and also, keeping the collected data private. In the past research, we suggested the use of similitude models, as compact models of data representation instead of the data itself. In this paper, we suggest the use of deep neural networks (DNN) as a data model to answer different types of queries. More specifically, we show that by building two models (generative network and auto-encoder) it is possible to answer approximately both statistical queries and membership queries without exposing the entire dataset.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5728-5736
Number of pages9
ISBN (Electronic)9781728108582
DOIs
StatePublished - 1 Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: 9 Dec 201912 Dec 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
Country/TerritoryUnited States
CityLos Angeles
Period9/12/1912/12/19

Keywords

  • BigData
  • Deep Learning
  • Similitude Models
  • Statistical Queries

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