MLDStore: DNNs as similitude models for sharing big data

Philip Derbeko, Shlomi Dolev, Ehud Gudes

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

2 Scopus citations

Abstract

The amount of data grows exponentially with time and the growth shows no signs of stopping. However, the data in itself is not useful until it can be processed, mined for information and queried. Thus, data sharing is a crucial component of modern computations. On the other hand, exposing the data might lead to serious privacy implications. In our 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 data models to answer different types of queries. In addition, we discuss ownership of the DNN models and how to retain the ownership of the model after sharing it.

Original languageEnglish
Title of host publicationCyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings
EditorsShlomi Dolev, Danny Hendler, Sachin Lodha, Moti Yung
PublisherSpringer Verlag
Pages93-96
Number of pages4
ISBN (Print)9783030209506
DOIs
StatePublished - 1 Jan 2019
Event3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019 - Beer Sheva, Israel
Duration: 27 Jun 201928 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11527 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019
Country/TerritoryIsrael
CityBeer Sheva
Period27/06/1928/06/19

Keywords

  • Big data
  • Deep neural networks
  • Similitude model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'MLDStore: DNNs as similitude models for sharing big data'. Together they form a unique fingerprint.

Cite this