BAdASS: Preserving privacy in behavioural advertising with applied secret sharing

Leon J. Helsloot; Gamze Tillem; Zekeriya Erkin

doi:10.1007/978-3-030-01446-9_23

BAdASS: Preserving privacy in behavioural advertising with applied secret sharing

Leon J. Helsloot, Gamze Tillem, Zekeriya Erkin

Cyber Security

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

5 Citations (Scopus)

Abstract

Online advertising forms the primary source of income for many publishers offering free web content by serving advertisements tailored to users’ interests. The privacy of users, however, is threatened by the widespread collection of data that is required for behavioural advertising. In this paper, we present BAdASS, a novel privacy-preserving protocol for Online Behavioural Advertising that achieves significant performance improvements over the state-of-the-art without disclosing any information about user interests to any party. BAdASS ensures user privacy by combining efficient secret-sharing techniques with a machine learning method commonly encountered in existing systems. Our protocol serves advertisements within a fraction of a second, based on highly detailed user profiles and widely used machine learning methods.

Original language	English
Title of host publication	Provable Security
Subtitle of host publication	12th International Conference, ProvSec 2018 - Proceedings
Editors	Joonsang Baek, Willy Susilo, Jongkil Kim
Place of Publication	Cham
Publisher	Springer
Pages	397-405
Number of pages	9
ISBN (Electronic)	978-3-030-01446-9
ISBN (Print)	978-3-030-01445-2
DOIs	https://doi.org/10.1007/978-3-030-01446-9_23
Publication status	Published - 2018
Event	ProvSec 2018: 12th International Conference on Provable Security - Jeju Island, Korea, Republic of Duration: 25 Oct 2018 → 28 Oct 2018 Conference number: 12 https://ssl.informatics.uow.edu.au/provsec2018/index.html

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Volume	11192
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	ProvSec 2018
Abbreviated title	ProvSEC
Country/Territory	Korea, Republic of
City	Jeju Island
Period	25/10/18 → 28/10/18
Internet address	https://ssl.informatics.uow.edu.au/provsec2018/index.html

Keywords

Behavioural advertising
Cryptography
Machine learning
Privacy
Secret sharing

Access to Document

10.1007/978-3-030-01446-9_23

Cite this

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title = "BAdASS: Preserving privacy in behavioural advertising with applied secret sharing",

abstract = "Online advertising forms the primary source of income for many publishers offering free web content by serving advertisements tailored to users{\textquoteright} interests. The privacy of users, however, is threatened by the widespread collection of data that is required for behavioural advertising. In this paper, we present BAdASS, a novel privacy-preserving protocol for Online Behavioural Advertising that achieves significant performance improvements over the state-of-the-art without disclosing any information about user interests to any party. BAdASS ensures user privacy by combining efficient secret-sharing techniques with a machine learning method commonly encountered in existing systems. Our protocol serves advertisements within a fraction of a second, based on highly detailed user profiles and widely used machine learning methods.",

keywords = "Behavioural advertising, Cryptography, Machine learning, Privacy, Secret sharing",

author = "Helsloot, {Leon J.} and Gamze Tillem and Zekeriya Erkin",

year = "2018",

doi = "10.1007/978-3-030-01446-9_23",

language = "English",

isbn = "978-3-030-01445-2",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "397--405",

editor = "Joonsang Baek and Willy Susilo and Jongkil Kim",

booktitle = "Provable Security",

note = "ProvSec 2018 : 12th International Conference on Provable Security, ProvSEC ; Conference date: 25-10-2018 Through 28-10-2018",

url = "https://ssl.informatics.uow.edu.au/provsec2018/index.html",

}

Helsloot, LJ, Tillem, G & Erkin, Z 2018, BAdASS: Preserving privacy in behavioural advertising with applied secret sharing. in J Baek, W Susilo & J Kim (eds), Provable Security: 12th International Conference, ProvSec 2018 - Proceedings. Lecture Notes in Computer Science, vol. 11192, Springer, Cham, pp. 397-405, ProvSec 2018, Jeju Island, Korea, Republic of, 25/10/18. https://doi.org/10.1007/978-3-030-01446-9_23

BAdASS: Preserving privacy in behavioural advertising with applied secret sharing. / Helsloot, Leon J.; Tillem, Gamze; Erkin, Zekeriya.
Provable Security: 12th International Conference, ProvSec 2018 - Proceedings. ed. / Joonsang Baek; Willy Susilo; Jongkil Kim. Cham: Springer, 2018. p. 397-405 (Lecture Notes in Computer Science; Vol. 11192).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - BAdASS

T2 - ProvSec 2018

AU - Helsloot, Leon J.

AU - Tillem, Gamze

AU - Erkin, Zekeriya

N1 - Conference code: 12

PY - 2018

Y1 - 2018

N2 - Online advertising forms the primary source of income for many publishers offering free web content by serving advertisements tailored to users’ interests. The privacy of users, however, is threatened by the widespread collection of data that is required for behavioural advertising. In this paper, we present BAdASS, a novel privacy-preserving protocol for Online Behavioural Advertising that achieves significant performance improvements over the state-of-the-art without disclosing any information about user interests to any party. BAdASS ensures user privacy by combining efficient secret-sharing techniques with a machine learning method commonly encountered in existing systems. Our protocol serves advertisements within a fraction of a second, based on highly detailed user profiles and widely used machine learning methods.

AB - Online advertising forms the primary source of income for many publishers offering free web content by serving advertisements tailored to users’ interests. The privacy of users, however, is threatened by the widespread collection of data that is required for behavioural advertising. In this paper, we present BAdASS, a novel privacy-preserving protocol for Online Behavioural Advertising that achieves significant performance improvements over the state-of-the-art without disclosing any information about user interests to any party. BAdASS ensures user privacy by combining efficient secret-sharing techniques with a machine learning method commonly encountered in existing systems. Our protocol serves advertisements within a fraction of a second, based on highly detailed user profiles and widely used machine learning methods.

KW - Behavioural advertising

KW - Cryptography

KW - Machine learning

KW - Privacy

KW - Secret sharing

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DO - 10.1007/978-3-030-01446-9_23

M3 - Conference contribution

SN - 978-3-030-01445-2

T3 - Lecture Notes in Computer Science

SP - 397

EP - 405

BT - Provable Security

A2 - Baek, Joonsang

A2 - Susilo, Willy

A2 - Kim, Jongkil

PB - Springer

CY - Cham

Y2 - 25 October 2018 through 28 October 2018

ER -

BAdASS: Preserving privacy in behavioural advertising with applied secret sharing

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