Efficient Parallel Implementation of Matrix Multiplication for Lattice-Based Cryptography on Modern ARM Processor

Park, Tae Hwan; Seo, Hwajeong; Kim, Jun-Sub; Park, Haeryong; Kim, Howon

doi:10.1155/2018/7012056

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…In [23], authors present an efficient matrix multiplication and vector addition for lattice-based cryptography. Using ARM NEON intrinsic functions and Raspberry Pi 3 Model B as hardware, they described the fastest method and time for multiplying two matrices (Z m×n 2 × Z n×l 2 ) is 93.91 ms where (m, n, l) := (1024, 536, 256).…”

Section: Discussionmentioning

confidence: 99%

A lightweight multi-party authentication in insecure reader-server channel in RFID-based IoT

Mamun

Miyaji

Luv

et al. 2020

Peer-to-Peer Netw. Appl.

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The rapid proliferation of Radio Frequency Identification (RFID) tags in the past decade has made tremendous impact on our daily lives. As part of Internet of Things (IoT), RFID technology ensures an efficient, secure and reliable system to identify tagged objects in supply chain environment such as manufacturing, automotive and healthcare. Several lightweight authentication solutions have been proposed to satisfy optimal security and privacy features of RFID communication. Hopper-Blum (HB) family of protocols that rely on the hard problem of Learning Parity with Noise (LPN) is a series of lightweight authentication protocol used to identify RFID tags. Our study shows that recent RFID authentication protocols from HB family that mostly focus on two party authentication such as tag-reader authentication, in general, cannot be applied directly to a three party authentication such as tag-reader-server authentication. In contrast to typical RFID authentication system, we consider the channel between the reader and back-end server insecure. We focus HB protocol and its variants and propose a modified protocol where the entire system is authenticated under LPN-based scheme.

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Section: Discussionmentioning

confidence: 99%

A lightweight multi-party authentication in insecure reader-server channel in RFID-based IoT

Mamun

Miyaji

Luv

et al. 2020

Peer-to-Peer Netw. Appl.

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show abstract

“…When it comes to high-performance big data analytics, it is regarded as the finest option. Spark Core uses YARN and Hadoop to distribute resources [60,61]. Additionally, it can retrieve data from the Hadoop Distributed File System (HDFS), which is used to read and store data and partition it into Resilient Distributed Datasets (RDDs).…”

Section: Parallel Computing Using Apache Spark Frameworkmentioning

confidence: 99%

Lightweight Computational Complexity Stepping Up the NTRU Post-Quantum Algorithm Using Parallel Computing

Elkabbany,

Ahmed,

Aslan

et al. 2023

Symmetry

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The Nth-degree Truncated polynomial Ring Unit (NTRU) is one of the famous post-quantum cryptographic algorithms. Researchers consider NTRU to be the most important parameterized family of lattice-based public key cryptosystems that has been established to the IEEE P1363 standards. Lattice-based protocols necessitate operations on large vectors, which makes parallel computing one of the appropriate solutions to speed it up. NTRUEncrypt operations contain a large amount of data that requires many repetitive arithmetic operations. These operations make it a strong candidate to take advantage of the high degree of parallelism. The main costly operation that is repeated in all NTRU algorithm steps is polynomial multiplication. In this work, a Parallel Post-Quantum NTRUEncrypt algorithm called PPQNTRUEncrypt is proposed. This algorithm exploits the capabilities of parallel computing to accelerate the NTRUEncrypt algorithm. Both analytical and Apache Spark simulation models are used. The proposed algorithm enhanced the NTRUEncrypt algorithm by approximately 49.5%, 74.5%, 87.6%, 92.5%, 93.4%, and 94.5%, assuming that the number of processing elements is 2, 4, 8, 12, 16, and 20 respectively.

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“…When it comes to high-performance big data analytics, it is regarded as the finest option. Spark core uses YARN and Hadoop to distribute resources [52][53]. Additionally, it can retrieve data from the Hadoop Distributed File System (HDFS), which is used to read and store data, and partition it into Resilient Distributed Datasets (RDDs).…”

Section: Parallel Computing Using Apache Spark Frameworkmentioning

confidence: 99%

Stepping up the NTRU-Post Quantum Algorithm Using Parallel Computing

ElKabbany

Aslan

Ibrahim³

2023

Preprint

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Nth degree Truncated polynomial Ring Unit (NTRU) is one of the famous post-quantum cryptographic algorithms. Researchers consider NTRU as the most important parameterized family of lattice-based public key cryptosystems that has been established to the IEEE P1363 standards. Lattice-based protocols necessitate operations on large vectors that makes parallel computing one of the appropriate solutions to speedup it. NTRUEncrypt operations contain a large amount of data that requires many repetitive arithmetic operations. These operations make it a strong candidate to take advantage of the high degree of parallelism. The main costly operation which is repeated in all NTRU algorithm steps is the polynomial multiplication. In this work, a Parallel Post Quantum NTRUEncrypt algorithm called PPQNTRUEncrypt is proposed. This algorithm exploits the capabilities of parallel computing to accelerate the NTRUEncrypt algorithm. Both analytical and Apache Spark simulation models are used. The proposed algorithm enhanced the NTRUEncrypt algorithm by approximately 49.5%, 74.5%, 87.6%, 92.5%, 93.4%, and 94.5% assuming that the number of processing elements is 2,4,8,12,16, and 20 respectively.

show abstract

Efficient Parallel Implementation of Matrix Multiplication for Lattice-Based Cryptography on Modern ARM Processor

Cited by 9 publications

References 18 publications

A lightweight multi-party authentication in insecure reader-server channel in RFID-based IoT

A lightweight multi-party authentication in insecure reader-server channel in RFID-based IoT

Lightweight Computational Complexity Stepping Up the NTRU Post-Quantum Algorithm Using Parallel Computing

Stepping up the NTRU-Post Quantum Algorithm Using Parallel Computing

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