On the Homomorphic Properties of Kyber and McEliece with Application to Post-Quantum Private Set Intersection

Crystals-Kyber and Classic-McEliece are two prominent post-quantum key encapsulation mechanisms (KEMs) designed to address the challenges posed by quantum computing to classical cryptographic schemes. While the former has been standardized by the National Institute of Standards and Technology (NIST), the latter is well-known for its exceptional robustness and as one of the finalists of the fourth round of post-quantum cryptography standardization. Private set intersection (PSI) is a privacy-preserving technique that enables two parties, each possessing a dataset, to compute the intersection of their sets without revealing anything else. This can be achieved thanks to homomorphic encryption (HE), which allows computations on encrypted data. In this paper, firstly, we study Kyber and McEliece, apart from being KEMs, as post-quantum public key encryption (PKE), and examine their homomorphic properties. Secondly, we design two different two-party PSI protocols that utilize the homomorphic capabilities of Kyber and McEliece. Thirdly, a practical performance evaluation under NIST’s security levels 1, 3, and 5 is conducted, focusing on three key metrics: storage overhead, communication overhead, and computation cost. Insights indicate that the Kyber-based PSI Protocol, which utilizes the multiplicative homomorphic property, is secure but less efficient. In contrast, the McEliece-based PSI protocol, while efficient in practice, raises concerns regarding its security as a homomorphic encryption scheme.

Authors

Institutions

Publication Details

Journal
Cryptography
Published
2025-10-20
DOI
https://doi.org/10.3390/cryptography9040066
Citations
1
Primary Topic
Cryptography and Data Security
Type
article
Field-Weighted Citation Impact
2.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

On the Homomorphic Properties of Kyber and McEliece with Application to Post-Quantum Private Set Intersection

Anas A. Abudaqa, Muhamad Felemban, Khaled Alshehri
1 citations
Cryptography
Cryptography and Data Security
2.48
article

On the Homomorphic Properties of Kyber and McEliece with Application to Post-Quantum Private Set Intersection

Anas A. Abudaqa, Muhamad Felemban, Khaled Alshehri
article en
1 citations

Abstract

Crystals-Kyber and Classic-McEliece are two prominent post-quantum key encapsulation mechanisms (KEMs) designed to address the challenges posed by quantum computing to classical cryptographic schemes. While the former has been standardized by the National Institute of Standards and Technology (NIST), the latter is well-known for its exceptional robustness and as one of the finalists of the fourth round of post-quantum cryptography standardization. Private set intersection (PSI) is a privacy-preserving technique that enables two parties, each possessing a dataset, to compute the intersection of their sets without revealing anything else. This can be achieved thanks to homomorphic encryption (HE), which allows computations on encrypted data. In this paper, firstly, we study Kyber and McEliece, apart from being KEMs, as post-quantum public key encryption (PKE), and examine their homomorphic properties. Secondly, we design two different two-party PSI protocols that utilize the homomorphic capabilities of Kyber and McEliece. Thirdly, a practical performance evaluation under NIST’s security levels 1, 3, and 5 is conducted, focusing on three key metrics: storage overhead, communication overhead, and computation cost. Insights indicate that the Kyber-based PSI Protocol, which utilizes the multiplicative homomorphic property, is secure but less efficient. In contrast, the McEliece-based PSI protocol, while efficient in practice, raises concerns regarding its security as a homomorphic encryption scheme.

CryptographyVol. 9(4)
King Fahd University of Petroleum and Minerals (SA), Qassim University (SA)
King Fahd University of Petroleum and Minerals
Openalex Percentile: Top 8%
Cryptography and Data Security
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.