Privacy-Preserving Data Aggregation with Probabilistic Range Validation

F.W. Dekker; Zekeriya Erkin

doi:10.1007/978-3-030-78375-4_4

Privacy-Preserving Data Aggregation with Probabilistic Range Validation

Cyber Security

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

61 Downloads (Pure)

Abstract

Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption of these protocols less likely.

We propose a privacy-preserving data aggregation protocol for calculating the sum of user inputs. Our protocol gives the aggregator confidence that all inputs are within a desired range. Instead of zero-knowledge proofs, our protocol relies on a probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Furthermore, our protocol is robust to user dropouts and, apart from the setup phase, it is non-interactive.

Original language	English
Title of host publication	International Conference on Applied Cryptography and Network Security
Editors	Kazue Sako, Nils Ole Tippenhauer
Place of Publication	Kamakura, Japan
Publisher	Springer Nature
Pages	79-98
Number of pages	20
Volume	2
Edition	19
ISBN (Electronic)	978-3-030-78375-4
ISBN (Print)	978-3-030-78374-7
DOIs	https://doi.org/10.1007/978-3-030-78375-4_4
Publication status	Published - 2021

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Nature
Number	1
Volume	12727
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Privacy
Data aggregation
Applied cryptography
Hypergraphs

Access to Document

10.1007/978-3-030-78375-4_4Licence: Unspecified

Pre-printAccepted author manuscript, 559 KBLicence: CC BY-NC-ND

Embargoed Document

Dekker_Erkin2021_Chapter_Privacy_PreservingDataAggregat
Final published version, 683 KB
Licence: Unspecified
Embargo ends: 9/12/21

Cite this

@inproceedings{65980240b9b64bf1b434d1db757e5401,

title = "Privacy-Preserving Data Aggregation with Probabilistic Range Validation",

abstract = "Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption of these protocols less likely. We propose a privacy-preserving data aggregation protocol for calculating the sum of user inputs. Our protocol gives the aggregator confidence that all inputs are within a desired range. Instead of zero-knowledge proofs, our protocol relies on a probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Furthermore, our protocol is robust to user dropouts and, apart from the setup phase, it is non-interactive.",

keywords = "Privacy, Data aggregation, Applied cryptography, Hypergraphs",

author = "F.W. Dekker and Zekeriya Erkin",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ",

year = "2021",

doi = "10.1007/978-3-030-78375-4_4",

language = "English",

isbn = "978-3-030-78374-7",

volume = "2",

series = "Lecture Notes in Computer Science",

publisher = "Springer Nature",

number = "1",

pages = "79--98",

editor = "Kazue Sako and Tippenhauer, {Nils Ole}",

booktitle = "International Conference on Applied Cryptography and Network Security",

address = "United States",

edition = "19",

}

Privacy-Preserving Data Aggregation with Probabilistic Range Validation. / Dekker, F.W.; Erkin, Zekeriya.

International Conference on Applied Cryptography and Network Security. ed. / Kazue Sako; Nils Ole Tippenhauer. Vol. 2 19. ed. Kamakura, Japan : Springer Nature, 2021. p. 79-98 (Lecture Notes in Computer Science; Vol. 12727, No. 1).

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

TY - GEN

T1 - Privacy-Preserving Data Aggregation with Probabilistic Range Validation

AU - Dekker, F.W.

AU - Erkin, Zekeriya

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2021

Y1 - 2021

N2 - Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption of these protocols less likely. We propose a privacy-preserving data aggregation protocol for calculating the sum of user inputs. Our protocol gives the aggregator confidence that all inputs are within a desired range. Instead of zero-knowledge proofs, our protocol relies on a probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Furthermore, our protocol is robust to user dropouts and, apart from the setup phase, it is non-interactive.

AB - Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption of these protocols less likely. We propose a privacy-preserving data aggregation protocol for calculating the sum of user inputs. Our protocol gives the aggregator confidence that all inputs are within a desired range. Instead of zero-knowledge proofs, our protocol relies on a probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Furthermore, our protocol is robust to user dropouts and, apart from the setup phase, it is non-interactive.

KW - Privacy

KW - Data aggregation

KW - Applied cryptography

KW - Hypergraphs

U2 - 10.1007/978-3-030-78375-4_4

DO - 10.1007/978-3-030-78375-4_4

M3 - Conference contribution

SN - 978-3-030-78374-7

VL - 2

T3 - Lecture Notes in Computer Science

SP - 79

EP - 98

BT - International Conference on Applied Cryptography and Network Security

A2 - Sako, Kazue

A2 - Tippenhauer, Nils Ole

PB - Springer Nature

CY - Kamakura, Japan

ER -

Privacy-Preserving Data Aggregation with Probabilistic Range Validation

Abstract

Publication series

Bibliographical note

Keywords

Access to Document

Embargoed Document

Fingerprint

Cite this