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

1 Citation (Scopus)

1301 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	Applied Cryptography and Network Security - 19th International Conference, ACNS 2021, Proceedings
Editors	Kazue Sako, Nils Ole Tippenhauer
Place of Publication	Kamakura, Japan
Publisher	Springer Nature
Pages	79-98
Number of pages	20
Volume	12727
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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12727 LNCS
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

Version of recordFinal published version, 683 KB

Cite this

Dekker, F. W., & Erkin, Z. (2021). Privacy-Preserving Data Aggregation with Probabilistic Range Validation. In K. Sako, & N. O. Tippenhauer (Eds.), Applied Cryptography and Network Security - 19th International Conference, ACNS 2021, Proceedings (19 ed., Vol. 12727, pp. 79-98). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12727 LNCS). Springer Nature. https://doi.org/10.1007/978-3-030-78375-4_4

Dekker, F.W. ; Erkin, Zekeriya. / Privacy-Preserving Data Aggregation with Probabilistic Range Validation. Applied Cryptography and Network Security - 19th International Conference, ACNS 2021, Proceedings. editor / Kazue Sako ; Nils Ole Tippenhauer. Vol. 12727 19. ed. Kamakura, Japan : Springer Nature, 2021. pp. 79-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Dekker, FW & Erkin, Z 2021, Privacy-Preserving Data Aggregation with Probabilistic Range Validation. in K Sako & NO Tippenhauer (eds), Applied Cryptography and Network Security - 19th International Conference, ACNS 2021, Proceedings. 19 edn, vol. 12727, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12727 LNCS, Springer Nature, Kamakura, Japan, pp. 79-98. https://doi.org/10.1007/978-3-030-78375-4_4

Privacy-Preserving Data Aggregation with Probabilistic Range Validation. / Dekker, F.W.; Erkin, Zekeriya.
Applied Cryptography and Network Security - 19th International Conference, ACNS 2021, Proceedings. ed. / Kazue Sako; Nils Ole Tippenhauer. Vol. 12727 19. ed. Kamakura, Japan: Springer Nature, 2021. p. 79-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12727 LNCS).

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

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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.

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VL - 12727

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

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BT - Applied Cryptography and Network Security - 19th International Conference, ACNS 2021, Proceedings

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CY - Kamakura, Japan

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Dekker FW , Erkin Z. Privacy-Preserving Data Aggregation with Probabilistic Range Validation. In Sako K, Tippenhauer NO, editors, Applied Cryptography and Network Security - 19th International Conference, ACNS 2021, Proceedings. 19 ed. Vol. 12727. Kamakura, Japan: Springer Nature. 2021. p. 79-98. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-78375-4_4

Privacy-Preserving Data Aggregation with Probabilistic Range Validation

Abstract

Publication series

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Graph-Based Reconstruction in Summation Sequences

Source code underlying the publication: Privacy-Preserving Data Aggregation with Probabilistic Range Validation

Cite this

Privacy-Preserving Data Aggregation with Probabilistic Range Validation

Abstract

Publication series

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Research output

Graph-Based Reconstruction in Summation Sequences

Datasets

Source code underlying the publication: Privacy-Preserving Data Aggregation with Probabilistic Range Validation

Cite this