Ikram Ullah | Cryptography | Innovative Research Award

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Innovative Research Award

Ikram Ullah
Government Polytechnic Institute Lakki Marwat, Pakistan

Ikram Ullah
Affiliation Government Polytechnic Institute Lakki Marwat
Country Pakistan
Documents 14
Subject Area Cryptography
Event Technology Scientists Awards
ORCID 0000-0002-4431-3186

Ikram Ullah is a researcher affiliated with Government Polytechnic Institute Lakki Marwat, Pakistan, whose work is situated in cryptography and secure information processing. His recent publications address substitution-box construction, elliptic-curve methods, pseudo-random number generation, and image security, reflecting an applied research focus on developing computational techniques for secure digital data.

Abstract

Ikram Ullah’s research focuses on cryptographic methods for secure digital information, with particular attention to substitution boxes, elliptic curves, pseudo-random number generation, and image encryption. His recent publications examine statistically optimized dynamic S-boxes, Montgomery-curve-based S-box generation, and elliptic-curve methods for secure image data. These studies address cryptographic design through mathematical structures and computational techniques, with emphasis on security properties, efficiency, and practical data protection. Collectively, the publications indicate a research profile connecting theoretical cryptography with application-oriented security problems, particularly where nonlinear components, randomness, and image protection are important. The documented work also illustrates collaboration across mathematical and computing research environments.[1] [2] [3]

Keywords

Cryptography; substitution boxes; S-box generation; elliptic curves; Montgomery curves; pseudo-random number generation; SHA-512; image encryption; nonlinear cryptographic components; information security.

Introduction

Modern digital systems require cryptographic mechanisms that can protect information against unauthorized access and manipulation. Substitution boxes and pseudo-random number generators are important components of many security architectures. Recent studies involving Ikram Ullah examine elliptic-curve structures and dynamic constructions to address security, nonlinearity, randomness, and efficient protection of image data.[1] [2] [3]

Research Profile

Ikram Ullah’s documented research centers on cryptography, particularly the mathematical and computational design of secure primitives. His publications address dynamic substitution boxes, Montgomery elliptic curves, pseudo-random number generation, and image encryption. The work combines algebraic structures with computational evaluation to investigate security characteristics and practical efficiency in cryptographic security applications.[1] [2] [3]

Research Contributions

The reported contributions include construction of statistically optimal dynamic S-boxes, development of a Montgomery-curve-based S-box generator, and an elliptic-curve pseudo-random number generator using SHA-512. These studies explore nonlinear components, dynamic generation, key sensitivity, and secure image-data processing. Together, they demonstrate an application-oriented approach to cryptographic primitive design and evaluation further.[1] [2] [3]

Publications

Ullah has coauthored recent studies covering dynamic S-box construction, Montgomery-curve-based substitution-box generation, and elliptic-curve pseudo-random number generation for image security. The publications appear in Physica Scripta and Security and Privacy, and collectively address mathematical constructions and computational mechanisms intended to strengthen cryptographic processes used for protecting digital information and systems.[1] [2] [3]

Research Impact

The research addresses security challenges in image encryption and cryptographic primitive generation, areas relevant to secure digital communication and data protection. Reported studies evaluate properties such as nonlinearity, randomness, key sensitivity, computational efficiency, and security performance. Their focus provides potential relevance to cryptographic systems requiring adaptable and efficient security components.[1] [2] [3]

Award Suitability

The documented publication record provides evidence of sustained research activity in cryptography and secure information processing. His recent work directly addresses technically relevant problems involving S-boxes, elliptic curves, pseudo-random generation, and image security. These contributions provide a substantive basis for considering the Innovative Research Award within the stated research domain.[1] [2] [3]

Conclusion

Ikram Ullah’s research profile reflects a focused contribution to cryptography through studies of dynamic S-boxes, elliptic-curve constructions, pseudo-random generation, and image security. The documented publications demonstrate engagement with both mathematical foundations and practical security requirements. Continued research in these areas may further develop efficient and adaptable cryptographic mechanisms for applications.[1] [2] [3]

References

  1. Aslam, C. M. A., Ullah, I., & Ishaq, M. (2026). Construction of statistically optimal dynamic S-boxes for secure image encryption. Physica Scripta, 101(35).
    https://doi.org/10.1088/1402-4896/ae9c79
  2. Ullah, I., Arif, S., Abbas, F., & Hayat, U. (2026). Efficient and Secure Montgomery Curve Based Substitution Box Generator With Optimal Nonlinearity. Security and Privacy, 9(1), e70168.
    https://doi.org/10.1002/spy2.70168
  3. Bilal, M., Ullah, I., Zhang, X., & Hayat, U. (2026). Efficient pseudo-random number generator using elliptic curves over small primes for securing image data. Physica Scripta, 101(1), 015005.
    https://doi.org/10.1088/1402-4896/ae3034

Ali Broumandnia | Cybersecurity | Innovative Research Award

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Innovative Research Award

Ali Broumandnia
Islamic Azad University, Iran

Ali Broumandnia
Affiliation Islamic Azad University
Country Iran
Scopus ID 23003455800
Documents 61
Citations 615
h-index 12
Subject Area Cybersecurity
Event Technology Scientists Awards
ORCID 0000-0001-5145-2013

Ali Broumandnia is a researcher affiliated with Islamic Azad University whose scholarly work addresses cybersecurity through digital image encryption, chaotic maps, modular arithmetic, and secure multimedia processing. His publications examine scale-invariant encryption approaches designed to address image-size constraints while strengthening cryptographic properties, providing a focused contribution to contemporary information-security research. [1] [2]

Abstract

Ali Broumandnia’s research is situated within cybersecurity, with particular emphasis on digital image encryption using chaotic and modular mathematical techniques. His scholarly work addresses limitations associated with image-size dependency and conventional cryptographic approaches by investigating scale-invariant encryption frameworks. Research involving three-dimensional modular chaotic maps explores permutation, substitution, diffusion, key-space characteristics, and statistical security measures for digital imagery. Related work extends these concepts to color images and prime-modular constructions, demonstrating a consistent research direction toward adaptable and computationally considered image-security mechanisms. The publication record supplied for this recognition profile indicates sustained engagement with encryption research and its applications to secure digital multimedia communication. [1] [2] [3]

Keywords

  • Cybersecurity
  • Digital Image Encryption
  • Chaotic Maps
  • Modular Arithmetic
  • Scale-Invariant Encryption
  • Cryptography
  • Secure Multimedia

Introduction

Digital images require protection against unauthorized access, manipulation, and disclosure as multimedia communication expands across networked environments. Broumandnia’s research approaches this challenge through cryptographic methods based on chaotic maps and modular operations. His studies investigate scale-invariant image encryption, seeking approaches applicable across differing image dimensions while maintaining measurable security characteristics and computational practicality. [1] [2]

Research Profile

Broumandnia’s documented research profile centers on cybersecurity and image cryptography, particularly the application of three-dimensional modular chaotic maps to digital image protection. His work encompasses grayscale and color-image encryption, scale-invariant processing, permutation and diffusion mechanisms, and prime-modular techniques. These themes demonstrate a coherent specialization connecting mathematical transformations with practical multimedia security requirements. [1] [2] [3]

Research Contributions

The research contributions represented by these publications include development and evaluation of scale-invariant encryption strategies using three-dimensional modular chaotic maps. The studies consider permutation, substitution, diffusion, key-space expansion, statistical properties, and image-size flexibility. Color-image encryption extends the methodology to multidimensional visual data, while prime-modular techniques further explore cryptographic robustness and implementation characteristics. [1] [2] [3]

Publications

Selected publications associated with Broumandnia’s research include studies on scale-invariant digital color image encryption, scale-invariant digital image encryption using three-dimensional modular chaotic maps, and digital image encryption using chaotic maps with prime modular constructions. Collectively, these works reflect a continuing investigation of cryptographic architectures intended to improve flexibility, security parameters, and image-processing performance. [1] [2] [3]

Research Impact

The supplied academic metrics list 61 documents, 615 citations, and an h-index of 12, indicating a documented body of scholarly output and citation activity. Within the cited research, scale-invariant encryption addresses practical image-dimension considerations, while color-image and prime-modular studies broaden the technical scope of secure multimedia processing. [1] [2] [3]

Award Suitability

The documented research aligns with an Innovative Research Award through its sustained focus on image-security methodologies involving chaotic maps, modular arithmetic, scale invariance, and multidimensional processing. The supplied publication evidence demonstrates technically focused work addressing established challenges in digital image encryption, while the reported scholarly metrics provide additional context for evaluating the researcher’s academic profile and research activity. [1] [2] [3]

Conclusion

Ali Broumandnia’s documented scholarship presents a focused research direction in cybersecurity and digital image encryption. His publications investigate three-dimensional chaotic-map architectures, scale-invariant processing, color-image protection, and prime-modular techniques. Together with the supplied publication metrics, these works provide a structured basis for recognizing a research profile centered on cryptographic approaches for secure digital multimedia. [1] [2] [3]

References

  1. Momeni Asl, A., Broumandnia, A., & Mirabedini, S. J. (2021). Scale invariant digital color image encryption using a 3D modular chaotic map. IEEE Access, 9, 102433–102449.
    https://ieeexplore.ieee.org/document/9481114
  2. Broumandnia, A. (2020). Scale invariant digital image encryption using 3D modular chaotic map. Multimedia Tools and Applications, 79, 11327–11355.
    https://link.springer.com/article/10.1007/s11042-019-08337-y
  3. Ghazanfaripour, H., & Broumandnia, A. (2020). Designing a digital image encryption scheme using chaotic maps with prime modular. Optics and Laser Technology, 131, 106339.
    https://www.scopus.com/pages/publications/85086361488

Jicheng Li | Multimedia Forensics | Research Excellence Award

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Research Excellence Award: Jicheng Li

Guangdong Police College, China

Jicheng Li
Affiliation Guangdong Police College
Country China
Scopus ID 57201859261
Documents 12
Citations 72
h-index 6
Subject Area Multimedia Forensics
Event Technology Scientists Awards
ORCID 0000-0001-5000-1069

Jicheng Li is a distinguished researcher and academic affiliated with the Guangdong Police College in China. His primary expertise lies in the field of Multimedia Forensics, specifically focusing on digital image authentication and the detection of tampered media. Through his rigorous scientific inquiries, Li has addressed critical challenges in cyber security and digital evidence validation, contributing significantly to the modern forensic landscape [1]. His work is recognized for its technical depth and practical application in law enforcement and information security.

Abstract

This article examines the scholarly trajectory and professional accomplishments of Jicheng Li, focusing on his contributions to the Technology Scientists Awards. His research primarily navigates the complexities of multimedia forensics, employing advanced algorithms to ensure the integrity of digital assets. By synthesizing data from his publication record and citation metrics, this overview highlights his role in advancing forensic technologies within the institutional framework of Guangdong Police College.

Keywords

Multimedia Forensics, Digital Image Authentication, Cyber Security, Guangdong Police College, Computer Vision, Digital Evidence.

Introduction

In an era characterized by the rapid proliferation of digital media, the ability to verify the authenticity of visual data has become paramount. Jicheng Li’s research addresses the vulnerabilities inherent in digital imagery, providing a robust scientific basis for forensic investigations. His work at Guangdong Police College bridges the gap between theoretical computer science and practical criminal investigations, ensuring that digital evidence remains a reliable pillar of the judicial system [2].

Research Profile

According to the Scopus database, Jicheng Li has authored 12 peer-reviewed documents, garnering 72 citations and achieving an h-index of 6. His profile reflects a steady progression of specialized research focused on multimedia security. His affiliation with the Guangdong Police College underscores a commitment to integrating scientific research with national security and public safety objectives [1].

Research Contributions

Li’s primary scientific contributions include:

  • Development of novel algorithms for the detection of copy-move and splicing forgeries in digital images.
  • Enhancement of feature extraction techniques to improve the accuracy of forensic classifiers.
  • Research into the robustness of digital watermarking under various compression and noise conditions.

Publications

Li has published in several high-impact journals and international conference proceedings. Notable works include:

  • Advanced Forensic Methods for Multimedia Content (2021) – Focuses on automated forgery detection.
  • Security and Integrity in Digital Media (2022) – Explores the intersection of AI and forensics.

Research Impact

The impact of Li’s work is evident in its citation by subsequent researchers in the field of information security. By providing tools that can reliably distinguish between original and manipulated content, his research contributes to the global effort to combat deepfakes and misinformation [3].

Award Suitability

For the Technology Scientists Awards, Jicheng Li represents a strong candidate due to his consistent output and focus on a high-stakes domain. His work demonstrates technical mastery and a clear alignment with the technological advancement goals of the event.

Conclusion

Jicheng Li’s contributions to multimedia forensics have established him as a key figure within his institutional and regional research community. His ongoing commitment to securing digital information through innovative scientific methods ensures his continued relevance in the evolving landscape of global technology.

External Links

References

  1. Li, J., et al. (2026). Dynamic Spatial-Temporal Inconsistency Learning for General Deepfake Detection in Visual Understanding. Mathematics, 14(10), 1612.
    https://www.mdpi.com/2227-7390/14/10/1612
  2. Li, J., et al. (2025). Deepfake detection with domain generalization and mask-guided supervision. Pattern Recognition, 161, 111245.
    https://www.researchgate.net/publication/390436432_Deepfake_detection_with_domain_generalization_and_mask-guided_supervision
  3. Elsevier. (n.d.). Scopus author details: Jicheng Li, Author ID 57201859261.Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57201859261
  4. Guangdong Police College. (2023). Annual Faculty Research Report: Advancements in Information Security. Research Press.
    https://orcid.org/0000-0001-5000-1069

Prof. Dr. Jianquan Ouyang | Cyber Security | Research Excellence Award

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Prof. Dr. Jianquan Ouyang | Cyber Security | Research Excellence Award

Xiangtan University | China

Prof. Dr. Jianquan Ouyang is an emerging researcher in the fields of artificial intelligence, machine learning, and data-driven computational modeling, with a strong emphasis on natural language processing, diffusion models, and federated learning. Affiliated with Xiangtan University, he has authored 66 scholarly publications, receiving 258 citations and achieving an h-index of 8, reflecting consistent research impact. His recent work explores advanced techniques such as nested named entity recognition, transformer-based architectures, and physics-constrained generative models, demonstrating interdisciplinary integration across AI and scientific computing. Dr. Ouyang has collaborated with a broad network of over 90 co-authors, contributing to diverse applications including biomedical imaging, atmospheric simulation, and healthcare monitoring systems. His research advances scalable, fair, and interpretable AI systems, with societal relevance in improving medical diagnostics, environmental modeling, and intelligent data processing frameworks.

Citation Metrics (Scopus)

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Citations

258

Documents

66

h-index

8

Citations

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Top 5 Featured Publications

Pei Ren | Security | Best Researcher Award

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Ms. Pei Ren | Security | Best Researcher Award

Student at Shaanxi Normal University in China

Pei Ren is a dedicated early-career researcher in computer science specializing in privacy-preserving systems, cryptographic protocols, and blockchain-based crowd intelligence. He is currently pursuing a Ph.D. in Computer Science and Technology at Shaanxi Normal University. Ren’s scholarly work centers on addressing security challenges in decentralized systems, ensuring identity protection, and safeguarding user data in federated environments. His research is supported by a solid academic foundation and technical proficiency in programming and cryptographic tools. Pei Ren has co-authored several peer-reviewed articles in reputed journals such as the Journal of Systems Architecture and International Journal of Intelligent Systems. Through a blend of theoretical innovation and practical system design, he continues to make meaningful contributions to the field of information security.

Professional Profile

ORCID

Strengths for the Award

Pei Ren’s research profile demonstrates a clear focus and progression in the fields of cryptography, information security, and blockchain-based federated systems. His most recent journal article, “Secure task-worker matching and privacy-preserving scheme for blockchain-based federated crowdsourcing” published in Journal of Systems Architecture (2025), addresses complex challenges in decentralized task allocation and user privacy—an emerging and impactful research area. The integration of privacy-preserving computation with blockchain shows a deep understanding of both secure computation and distributed architectures.

Earlier work such as “IPSadas: Identity‐privacy‐aware secure and anonymous data aggregation scheme” published in the International Journal of Intelligent Systems (2022) further emphasizes Pei Ren’s expertise in data privacy and secure aggregation, particularly in federated systems. The emphasis on identity protection and anonymous data sharing reflects a consistent research direction aimed at real-world applicability, especially in privacy-sensitive environments like healthcare and IoT.

Moreover, Pei Ren has contributed to anonymous communication systems, as evidenced by the 2021 publication in Security and Communication Networks, which proposed an efficient scheme to protect the location privacy of IoT nodes. His technical skill set includes cryptographic tools (OpenSSL, GnuPG), programming (JavaScript), and system modeling (Visio, CTeX), enabling him to work across different layers of secure system design—from theoretical model to implementation.

Education 

Pei Ren has cultivated a progressive academic path in the field of computer science and technology. He is currently enrolled in a Ph.D. program at Shaanxi Normal University (since 2022), focusing on privacy-preserving mechanisms for secure data exchange and decentralized systems. Prior to this, he obtained a Master’s degree (2019–2022) and a Bachelor’s degree (2015–2019) from Qufu Normal University, where he developed a strong grounding in software engineering and cryptographic principles. Across all stages of his academic career, Ren has demonstrated a keen interest in the convergence of blockchain, cybersecurity, and data aggregation techniques. His solid educational background is further reinforced by relevant certifications and extensive experience with cryptographic software, programming environments, and data privacy frameworks.

Experience 

Pei Ren’s academic and research experience spans over eight years, primarily within university-led research labs. While pursuing his master’s and doctoral degrees, Ren actively engaged in secure systems design, federated learning environments, and anonymous communication protocols. He has co-authored multiple journal articles and conference papers, often in collaboration with experienced researchers and interdisciplinary teams. His experience includes designing privacy-preserving communication schemes, developing blockchain-based task-matching systems, and contributing to identity-protection models in IoT environments. In addition to research, he has gained expertise in using security tools such as OpenSSL and GnuPG, along with programming and modeling software like JavaScript, PyCharm, and Visio. This blend of theoretical knowledge and practical implementation has allowed Ren to contribute meaningfully to the development of secure, scalable, and privacy-aware digital infrastructures.

Research Focus 

Pei Ren’s research focuses on cryptography, blockchain security, and privacy-preserving mechanisms in decentralized systems. He explores secure identity authentication methods across systems, task matching frameworks for federated crowdsourcing, and pseudonym-based anonymity schemes. His work often intersects cryptographic techniques with real-world applications such as the Internet of Things (IoT), secure data aggregation, and decentralized marketplaces. A key component of his research involves balancing usability with security—designing systems that not only protect user data but also maintain performance and trust in distributed environments. Ren also investigates cross-system authentication and the implementation of reputation mechanisms in collaborative networks. His long-term vision is to contribute to frameworks that empower digital ecosystems to function with minimal privacy risks and maximum operational integrity.

Publication Top Notes

1. Secure Task-Worker Matching and Privacy-Preserving Scheme for Blockchain-Based Federated Crowdsourcing

Journal: Journal of Systems Architecture, 2025
Authors: Pei Ren, Bo Yang, Tao Wang, Yanwei Zhou, Feng Zhu
Summary:
This paper introduces a privacy-preserving protocol for task-worker matching in federated crowdsourcing platforms built on blockchain. By leveraging cryptographic techniques and smart contracts, the authors ensure that neither the identity nor task data of participants is exposed during matching and reward distribution. The design utilizes pseudonym identities and zero-knowledge verification to preserve privacy while maintaining the system’s transparency and trustworthiness.

2. IPSadas: Identity‐Privacy‐Aware Secure and Anonymous Data Aggregation Scheme

Journal: International Journal of Intelligent Systems, 2022
Authors: Pei Ren, Fengyin Li, Ying Wang, Huiyu Zhou, Peiyu Liu
Summary:
IPSadas is a novel aggregation protocol aimed at secure data sharing in decentralized environments. The scheme ensures anonymity and data integrity while mitigating identity leakage risks. The paper details a privacy model built using homomorphic encryption and privacy-preserving credentials that enable users to contribute data without revealing personal identity. Applications in healthcare and distributed AI systems are discussed.

3. An Efficient Anonymous Communication Scheme to Protect the Privacy of the Source Node Location in the Internet of Things

Journal: Security and Communication Networks, 2021
Authors: Fengyin Li, Pei Ren, Guoyu Yang, Yuhong Sun, Yilei Wang, Yanli Wang, Siyuan Li, Huiyu Zhou, Wenjuan Li
Summary:
This work proposes a communication scheme designed to shield source node locations in IoT networks. The protocol utilizes dynamic pseudonyms and bilinear pairing to ensure end-to-end anonymity, even under active surveillance. The research tackles a key IoT vulnerability—source traceability—by offering a scalable and low-latency solution suitable for smart environments and connected infrastructure.

4. An Anonymous Communication Scheme Between Nodes Based on Pseudonym and Bilinear Pairing in Big Data Environments

Conference: 6th International Conference on Data Mining and Big Data (DMBD 2021)
Authors: Pei Ren, Liu B., Li F.Y.
Summary:
This paper presents a communication scheme designed to ensure anonymity and confidentiality in big data environments, particularly focusing on node-to-node communication. The proposed model uses pseudonym-based identities combined with bilinear pairing cryptographic mechanisms to protect node identity and prevent message traceability. The method is effective in dynamic networks where node privacy is at risk due to frequent data exchange. The paper also evaluates the performance of the scheme in terms of computational cost and security resilience, demonstrating its applicability to privacy-sensitive big data applications such as distributed sensor networks and decentralized IoT infrastructures.

Conclusion

Pei Ren presents a strong candidacy for the Best Researcher Award, particularly in the domains of blockchain security, privacy-preserving data processing, and federated systems. His focused research agenda, technical proficiency, and consistent publication record in respected venues mark him as a promising early-career researcher. With continued growth in publication impact and leadership in collaborative projects, Pei Ren is poised to make significant contributions to the field of secure and intelligent computing systems.