MLDStore: DNNs as similitude models for sharing big data

Philip Derbeko; Shlomi Dolev; Ehud Gudes

doi:10.1007/978-3-030-20951-3_7

MLDStore: DNNs as similitude models for sharing big data

Philip Derbeko, Shlomi Dolev, Ehud Gudes

Department of Computer Science

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	American English
Title of host publication	Cyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings
Editors	Shlomi Dolev, Danny Hendler, Sachin Lodha, Moti Yung
Publisher	Springer Verlag
Pages	93-96
Number of pages	4
ISBN (Print)	9783030209506
DOIs	https://doi.org/10.1007/978-3-030-20951-3_7
State	Published - 1 Jan 2019
Event	3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019 - Beer Sheva, Israel Duration: 27 Jun 2019 → 28 Jun 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11527 LNCS

Conference

Conference	3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019
Country/Territory	Israel
City	Beer Sheva
Period	27/06/19 → 28/06/19

Keywords

Big data
Deep neural networks
Similitude model

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-20951-3_7

Cite this

Derbeko, P., Dolev, S., & Gudes, E. (2019). MLDStore: DNNs as similitude models for sharing big data. In S. Dolev, D. Hendler, S. Lodha, & M. Yung (Eds.), Cyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings (pp. 93-96). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11527 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-20951-3_7

Derbeko, Philip ; Dolev, Shlomi ; Gudes, Ehud. / MLDStore : DNNs as similitude models for sharing big data. Cyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings. editor / Shlomi Dolev ; Danny Hendler ; Sachin Lodha ; Moti Yung. Springer Verlag, 2019. pp. 93-96 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4880b7cf641e4d4d8085f321207b1b48,

title = "MLDStore: DNNs as similitude models for sharing big data",

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.",

keywords = "Big data, Deep neural networks, Similitude model",

author = "Philip Derbeko and Shlomi Dolev and Ehud Gudes",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019 ; Conference date: 27-06-2019 Through 28-06-2019",

year = "2019",

month = jan,

day = "1",

doi = "10.1007/978-3-030-20951-3_7",

language = "American English",

isbn = "9783030209506",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "93--96",

editor = "Shlomi Dolev and Danny Hendler and Sachin Lodha and Moti Yung",

booktitle = "Cyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings",

address = "Germany",

}

Derbeko, P, Dolev, S & Gudes, E 2019, MLDStore: DNNs as similitude models for sharing big data. in S Dolev, D Hendler, S Lodha & M Yung (eds), Cyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11527 LNCS, Springer Verlag, pp. 93-96, 3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019, Beer Sheva, Israel, 27/06/19. https://doi.org/10.1007/978-3-030-20951-3_7

MLDStore: DNNs as similitude models for sharing big data. / Derbeko, Philip; Dolev, Shlomi; Gudes, Ehud.
Cyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings. ed. / Shlomi Dolev; Danny Hendler; Sachin Lodha; Moti Yung. Springer Verlag, 2019. p. 93-96 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11527 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - MLDStore

T2 - 3rd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019

AU - Derbeko, Philip

AU - Dolev, Shlomi

AU - Gudes, Ehud

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Big data

KW - Deep neural networks

KW - Similitude model

UR - http://www.scopus.com/inward/record.url?scp=85068207979&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-20951-3_7

DO - 10.1007/978-3-030-20951-3_7

M3 - Conference contribution

SN - 9783030209506

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 93

EP - 96

BT - Cyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings

A2 - Dolev, Shlomi

A2 - Hendler, Danny

A2 - Lodha, Sachin

A2 - Yung, Moti

PB - Springer Verlag

Y2 - 27 June 2019 through 28 June 2019

ER -

Derbeko P, Dolev S, Gudes E. MLDStore: DNNs as similitude models for sharing big data. In Dolev S, Hendler D, Lodha S, Yung M, editors, Cyber Security Cryptography and Machine Learning - 3rd International Symposium, CSCML 2019, Proceedings. Springer Verlag. 2019. p. 93-96. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-20951-3_7

MLDStore: DNNs as similitude models for sharing big data

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Cite this