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Publisher: Science and Technology Publications, Lda

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    Threshold Structure-Preserving Signatures With Randomizable Key
    (Science and Technology Publications, Lda, 2025) Ağırtaş, A.R.; Çelik, E.; Kocaman, S.; Sulak, F.; Yayla, Oğuz
    Digital signatures confirm message integrity and signer identity, but linking public keys to identities can cause privacy concerns in anonymized settings. Signatures with randomizable keys can break this link, preserving verifiability without revealing the signer. While effective for privacy, complex cryptographic systems need to be modular structured for efficient implementation. Threshold structure-preserving signatures enable modular, privacy-friendly protocols. This work combines randomizable keys with threshold structure-preserving signatures to create a valid, modular, and unlinkable foundation for privacy-preserving applications. © 2025 by Paper published under CC license (CC BY-NC-ND 4.0).
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    Citation - Scopus: 1
    Similarity-Inclusive Link Prediction With Quaternions
    (Science and Technology Publications, Lda, 2021) Kurt,Z.; Gerek,Ö.N.; Bilge,A.; Özkan,K.
    This paper proposes a Quaternion-based link prediction method, a novel representation learning method for recommendation purposes. The proposed algorithm depends on and computation with Quaternion algebra, benefiting from the expressiveness and rich representation learning capability of the Hamilton products. The proposed method depends on a link prediction approach and reveals the significant potential for performance improvement in top-N recommendation tasks. The experimental results indicate the superior performance of the approach using two quality measurements – hits rate, and coverage - on the Movielens and Hetrec datasets. Additionally, extensive experiments are conducted on three subsets of the Amazon dataset to understand the flexibility of this algorithm to incorporate different information sources and demonstrate the effectiveness of Quaternion algebra in graph-based recommendation algorithms. The proposed algorithms obtain comparatively higher performance, they are improved with similarity factors. The results show that the proposed quaternion-based algorithm can effectively deal with the deficiencies in graph-based recommender system, making it a preferable alternative among the other available methods. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.