research-article

Communication-Efficient Inner Product Private Join and Compute with Cardinality

Authors:
Koji Chida

Gunma University, Japan

Gunma University, Japan

0000-0002-7705-5996
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,
Koki Hamada

NTT Social Informatics Laboratories, Japan

NTT Social Informatics Laboratories, Japan

0000-0002-8863-6809
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,
Atsunori Ichikawa

NTT Social Informatics Laboratories, Japan

NTT Social Informatics Laboratories, Japan

0000-0001-8013-7071
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,
Masanobu Kii

NTT Social Informatics Laboratories, Japan

NTT Social Informatics Laboratories, Japan

0000-0003-1323-0983
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,
Junichi Tomida

NTT Social Informatics Laboratories, Japan

NTT Social Informatics Laboratories, Japan

0000-0001-7995-877X
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ASIA CCS '23: Proceedings of the 2023 ACM Asia Conference on Computer and Communications SecurityJuly 2023Pages 678–688https://doi.org/10.1145/3579856.3582826

Published:10 July 2023Publication History

ASIA CCS '23: Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security

Pages 678–688

ABSTRACT

Private join and compute (PJC) is a paradigm where two parties owing their private database securely join their databases and compute a function over the combined database. Inner product PJC, introduced by Lepoint et al. (Asiacrypt’21), is a class of PJC that has a wide range of applications such as secure analysis of advertising campaigns. In this computation, two parties, each of which has a set of identifier-value pairs, compute the inner product of the values after the (inner) join of their databases with respect to the identifiers. They proposed inner product PJC protocols that are specialized for the unbalanced setting where the input sizes of both parties are significantly different and not suitable for the balanced setting where the sizes of two inputs are relatively close.

We propose an inner product PJC protocol that is much more efficient than that by Lepoint et al. for balanced inputs in the setting where both parties are allowed to learn the intersection size additionally. Our protocol can be seen as an extension of the private intersection-sum protocol based on the decisional Diffie-Hellman assumption by Ion et al. (EuroS&P’20) and is especially communication-efficient as the private intersection-sum protocol. In the case where both input sizes are 216, the communication cost of our inner-product PJC protocol is 46 × less than that of the inner product PJC protocol by Lepoint et al.

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Index Terms

Communication-Efficient Inner Product Private Join and Compute with Cardinality
1. Security and privacy
  1. Cryptography

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Published in
ASIA CCS '23: Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security
July 2023
1066 pages
ISBN:9798400700989
DOI:10.1145/3579856
Editors:
Joseph Liu
Monash University, Australia
,
Yang Xiang
Swinburne University of Technology, Australia
,
Surya Nepal
Data61, Australia
,
Gene Tsudik
University of California Irvine, USA
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- Published: 10 July 2023
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Author Tags
inner product
private intersection-sum
private join and compute
private set intersection
secure computation
two-party computation
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Communication-Efficient Inner Product Private Join and Compute with Cardinality

ASIA CCS '23: Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security

ABSTRACT

References

Cited By

Index Terms

Recommendations

Efficient Batched Oblivious PRF with Applications to Private Set Intersection

Linear Complexity Private Set Intersection for Secure Two-Party Protocols

Authenticated Garbling and Efficient Maliciously Secure Two-Party Computation