@inproceedings{5781e582cefb404190de43488e29e984,
title = "Deep Neural Networks as Similitude Models for Sharing Big Data",
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.",
keywords = "BigData, Deep Learning, Similitude Models, Statistical Queries",
author = "Philip Derbeko and Shlomi Dolev and Ehud Gudes",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",
year = "2019",
month = dec,
day = "1",
doi = "https://doi.org/10.1109/BigData47090.2019.9006313",
language = "American English",
series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
pages = "5728--5736",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",
booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
}