Cryptographic Techniques Research Articles

Privacy-Preserving Data Sharing Platform

In today's data-driven healthcare landscape, the secure sharing of sensitive medical information is essential for improving patient care, facilitating medical research, and advancing healthcare outcomes. However, ensuring the integrity, confidentiality, and privacy of patient data poses significant challenges, particularly in the context of big data environments. This presents a comprehensive framework for privacy-preserving data sharing in healthcare, leveraging a combination of cryptographic techniques, encryption, and secure computation protocols. The framework encompasses various privacy-preserving mechanisms, including Differential Privacy with Data Perturbation, Secure Multi-Party Computation (SMPC), and Homomorphic Encryption, to protect sensitive healthcare data from unauthorized access and disclosure. By implementing state-of-the-art privacy-preserving techniques, the framework aims to enable secure data sharing among multiple parties while complying with regulatory requirements such as HIPAA and GDPR. Additionally, the paper discusses the project scope, which includes cryptography, encryption, decryption, integrity, confidentiality, privacy, policies, procedures, security, and secure data sharing infrastructure. The proposed framework provides a practical solution for healthcare organizations and research institutions to collaborate on data-driven initiatives while safeguarding patient privacy and maintaining trust. Evaluation of the framework's effectiveness and performance metrics is conducted to validate its feasibility and efficacy in real-world healthcare settings. Keywords: Privacy-preserving data sharing, Differential Privacy, Data Perturbation, Secure Multi-Party Computation (SMPC)

Privacy Preservation of Electronic Health Records in the Modern Era: A Systematic Survey

Building a secure and privacy-preserving health data sharing framework is a topic of great interest in the healthcare sector, but its success is subject to ensuring the privacy of user data. We clarified the definitions of privacy, confidentiality and security (PCS) because these three terms have been used interchangeably in the literature. We found that researchers and developers must address the differences of these three terms when developing electronic health record (EHR) solutions. We surveyed 130 studies on EHRs, privacy-preserving techniques, and tools that were published between 2012 and 2022, aiming to preserve the privacy of EHRs. The observations and findings were summarized with the help of the identified studies framed along the survey questions addressed in the literature review. Our findings suggested that the usage of access control, blockchain, cloud-based, and cryptography techniques is common for EHR data sharing. We summarized the commonly used strategies for preserving privacy that are implemented by various EHR tools. Additionally, we collated a comprehensive list of differences and similarities between PCS. Finally, we summarized the findings in a tabular form for all EHR tools and techniques and proposed a fusion of techniques to better preserve the PCS of EHRs.

Research on Various Cryptography Techniques

Cryptography is utilized to make secure data transmission over networks. The algorithm called for cryptography should meet the conditions of authentication, confidentiality, integrity and non-repudiation. Cryptography is a technique used from decenniums to secure and forfend the information and send the data from one place to another without the trepidation of having been read out by some unauthorized and unauthenticated denotes. Several ways has been developed in this field to make the information more secure and evade trespassing. However these methods may have some loopholes or shortcoming which leads to the leakage of information and thus raising a question of information security. The cryptographic technique is utilized not only to provide the security but additionally it deals with data integrity, confidentiality and non-repudiation issues. To safeguard data during transmission or storage, sundry algorithms and methods have been developed in the field of security. A wide range of cryptography approaches are employed, each with its own set of strengths and inhibitions that are acclimated to provide data security. Cryptography can be defined as techniques that cipher data, depending on categorical algorithms that make the data unreadable to the human ocular perceiver unless decrypted by algorithms that are predefined by the sender. It encrypts data utilizing a set of algorithms such as symmetric and asymmetric algorithms. These encryption methods vary in terms of vitality, celerity, and utilization of resources (CPU utilization, recollection, and power). It is utilized to bulwark personal identifiable information (PII) and other confidential data, authenticate identities, avert document tampering, and build trust between servers. Cryptography is one of the most paramount techniques utilized by digital businesses to safeguard the systems that store their most valuable asset – data – whether it is at rest or in kinetic Customer PII, employee PII, perspicacious property, company strategies, and any other confidential information are examples of data. As a result, cryptography is a vital infrastructure, as the aegis of sensitive data increasingly relies on cryptographic solutions. In this paper I have discussed various cryptographic techniques and the inhibitions of those techniques as well. Some cryptographic algorithms are briefly described and their impact on the information is additionally mentioned.

Challenges and Future Trends of Cryptography in Internet of Things

The incorporation of cryptographic techniques is crucial for guaranteeing data privacy and security processed additionally sent inside IOT ecosystems, particularly as the IOT keeps growing. Examining problems including resource limitations, scalability, and the dynamic nature of IOT environments, this research paper explores the complex obstacles that cryptographic solutions confront considering the IOT. Lightweight cryptography, post-quantum cryptography, and blockchain integration are some of the new trends and future prospects in cryptographic research that are examined in this study in an effort to address these issues. This work offers a useful viewpoint for scholars, practitioners, and legislators engaged in the constantly changing field of cryptographic protocols inside the IOT framework by clarifying current problems and projecting future advancements. Keywords— Cryptography, Internet of Things (IOT), Security,Challenges

Remote Sensing Images Secure Distribution Scheme Based on Deep Information Hiding

To ensure the security of highly sensitive remote sensing images (RSIs) during their distribution, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve the secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on an encoder–decoder network architecture with Parallel Attention Mechanism (PAM) by adversarial training is proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN), and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSIs and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected.

Hybrid Cryptographic Approach for Data Security Using Elliptic Curve Cryptography for IoT

The Internet of Things (IoT) technology has changed the contemporary digital world. Devices connected to the IoT have sensors embedded within them. All these devices are purposely connected to share data among them through the Internet. Data sharing among IoT devices needs some security protocols to maintain the privacy and confidentiality of information. IoT devices have less computing power to perform various operations of a cryptographic process. So, there is a need of cryptographic approach to reduce the computational complexity for resource-constrained devices and provide data security. However, storing data over the cloud server also reduces storage overhead, but data transmission via the cloud is not always secure. Data integrity and authentication can be compromised because the end user can only access the data with the help of a cloud server. To ensure the security and integrity of the data, various cryptographic techniques are used. Therefore, in this paper, we propose a secure and optimized hybrid cryptographic scheme for the secure sharing of data by combining Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC). To ensure authentication and data integrity, the proposed scheme primarily uses the Message Authentication Code (MAC). The encrypted messages are stored on a cloud server to reduce storage overhead. The experimental findings demonstrate that the proposed scheme is effective and produces superior results as compared to existing approaches.

An Enhanced AES-ECC model with Key Dependent Dynamic S-Box for the Security of Mobile Applications using Cloud Computing

The spectacular growth of computational clouds has drawn attention and enabled intensive computing on client devices with constrained resources. Smart phones mostly use the data centre demand service model to provide data to computationally intensive applications. It is challenging to outsource sensitive and private information to distant data centres due to growing concerns about data privacy and security. Therefore, conventional security algorithms have to be upgraded to address the brand-new security concerns that have emerged in the cloud environment. The Advanced Encryption Standard (AES) algorithm encrypts and decrypts messages using the four steps, namely Sub-Bytes, Shift-Rows, Mix-Columns, and Add Round Key. It has been changed so that the Sub-byte transformation depends on the round key. Making the S box round key dependent is intended to make it such that even a little change in the key will have a big impact on the cipher text. The avalanche effect and execution time of the standard and modified AES algorithms are implemented and assessed. In order to suggest a safe and efficient solution, the study also aims to modify the shift rows and the mix column phases of AES. Using cryptographic techniques such as elliptic curve cryptography (ECC), several protocols and algorithms have been developed to guarantee the security and integrity of the data. The proposed solution combines the enhanced and updated The Advanced Encryption Standard (AES) technology with the ECC to guarantee data secrecy. The avalanche effect approach is used in this study to explore and analyze the strength and quality of the new s-box. The results demonstrated that each encryption operation generates a unique cipher text. A strong avalanche effect was also achieved with the redesigned dynamic s-box using irreducible polynomials, surpassing the strict avalanche criterion (SAC). The results show that the proposed method ECC-EAESKDS Elliptic Curve Cryptography-Enhanced AES with Key Dependent S box is efficient and yields better results than the alternatives. According to the suggested security architecture, cloud users may control data protection and integrity in a secure manner.

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A Survey on Quantum-Cryptographic Image Encryption for Secure Storage

The aim of this paper is to explore and implement cutting-edge quantum cryptographic techniques to enhance the security of image data storage. Leveraging principles from quantum computing and cryptography, the project seeks to develop an advanced image encryption system that ensures unprecedented levels of security. By harnessing the unique properties of quantum mechanics, such as superposition and entanglement, the proposed system will establish a robust foundation for encrypting images, surpassing the limitations of classical cryptographic methods. The utilization of quantum key distribution protocols will add an additional layer of security, making it virtually impossible for adversaries to intercept or compromise sensitive image data during storage. This endeavor not only addresses the escalating cybersecurity concerns surrounding image storage but also contributes to the advancement of quantum technologies in practical applications. The project envisions a novel paradigm in image encryption, fostering a secure environment for sensitive visual information across various domains, including healthcare, finance, and defense. Keywords—Quantum Mechanics, Quantum Computing, cryptography, secure.

ReActHE: A homomorphic encryption friendly deep neural network for privacy-preserving biomedical prediction

The growing distribution of deep learning models to individuals’ devices on sensitive healthcare data introduces challenging privacy and security problems when computation is being operated on an untrusted server. Homomorphic encryption (HE) is one of the appropriate cryptographic techniques to provide secure machine learning computation by directly computing over encrypted data, so that allows the data owner and model owner to outsource processing of sensitive information to an untrusted server without leaking any information about the data. However, most current HE schemes only support limited arithmetic operations, which significantly hinder their applications to implement a secure deep learning algorithm, especially on the nonlinear activation function of a deep neural network. In this paper, we develop a novel HE-friendly deep neural network, named REsidue ACTivation HE (ReActHE), to implement a precise and privacy-preserving algorithm with a non-approximating HE scheme on the activation function. We consider a residue activation strategy with a scaled power activation function in a deep neural network for HE-friendly nonlinear activation. Moreover, we propose a residue activation network structure to constrain the latent space in the training process to alleviate the optimization difficulty. We comprehensively evaluate the proposed ReActHE method using various biomedical datasets and widely-used image datasets. Our results demonstrate that ReActHE outperforms other alternative solutions to secure machine learning with HE and achieves low approximation errors in classification and regression tasks.

E2EE enhanced patient-centric blockchain-based system for EHR management.

To secure sensitive medical records in the healthcare clouds, this paper proposes an End-to-End Encryption (E2EE) to enhance a patient-centric blockchain-based system for electronic health record (EHR) management. The suggested system with a focus on the patient enables individuals to oversee their medical records within various involved parties by authorizing or withdrawing permission for access to their records. Utilizing the inter-planetary file system (IPFS) for record storage is chosen due to its decentralized nature and its ability to guarantee the unchangeability of records. Then an E2EE enhancement maintains the medical data integrity using dual level-Hybrid encryption: symmetric Advanced Encryption Standard (AES) and asymmetric Elliptic Curve Cryptography (ECC) cryptographic techniques. The proposed system is implemented using the Ethereum blockchain system for EHR data sharing and integration utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions over the IPFS cloud. The proposed system performance is evaluated in a working system prototype. For different file sizes between 512 KB to 100 MB, the performance metrics used to evaluate the proposed system were the time consumed for generating key, encryption, and decryption. The results demonstrate the proposed system's superiority over other cutting-edge systems and its practical ability to share secure health data in cloud environments.

Audit Trail And Transparency In Cloud-Based Auditing

In the era of digital transformation, organizations increasingly rely on cloud-based solutions for their auditing processes to enhance efficiency, accessibility, and scalability. This research paper explores the pivotal role of audit trails and transparency in cloud-based auditing, addressing the challenges, benefits, and implications for ensuring robust security and accountability in the ever-evolving landscape of cloud computing. The paper begins by delving into the fundamental concepts of audit trails and transparency in the context of cloud-based auditing. Audit trails serve as chronological records of activities, providing a comprehensive view of actions performed within the auditing system. Transparency, on the other hand, emphasizes openness and visibility in the auditing process, fostering trust among stakeholders. Together, these elements contribute to the establishment of a secure and accountable auditing environment. One of the primary challenges in cloud-based auditing is the dynamic nature of cloud computing environments. The paper examines how the inherent complexities of cloud architectures, such as multi-tenancy and virtualization, can pose challenges to maintaining an effective audit trail. Furthermore, the dynamic allocation of resources and the use of third-party services demand innovative solutions to ensure the integrity and completeness of audit records. To address these challenges, the paper proposes a framework that combines advanced cryptographic techniques, secure logging mechanisms, and real-time monitoring to strengthen the audit trail in cloud-based environments. Emphasizing the importance of encryption and hashing algorithms, the framework aims to safeguard sensitive information while maintaining the transparency required for auditing purposes. Additionally, the integration of machine learning algorithms for anomaly detection enhances the system’s ability to identify and respond to irregularities promptly.

Enhanced cryptographic performance and security using optimized edward-elgamal signature scheme for IoT and blockchain applications

Abstract The rapid proliferation of Internet of Things (IoT) devices and blockchain technology requires robust and efficient cryptographic solutions to ensure secure communication and data integrity. This paper presents an optimized Edward-Elgamal signature algorithm that uses Edward curved signature storage mechanism to improve performance in IoT and blockchain environments to overcome the limitations of schemes, especially Elliptic Curve Digital Signature Algorithm (ECDSA) and Hyper-ECDSA. The proposed method achieved faster signature generation times, with a 33% improvement over ECDSA and a 25% improvement over Hyper-ECDSA, making it more suitable for faster applications. The validation times of the optimized systems were relatively low, with a 32% improvement over ECDSA and a 24% improvement over Hyper-ECDSA. The efficiency of the proposed system was significantly higher, showing a 51% improvement over ECDSA and a 35% improvement over Hyper-ECDSA. The latency was reduced by 33% compared to ECDSA and 26% compared to Hyper-ECDSA, indicating the effectiveness of the optimized system in terms of time-related performance. This paper contributes to the advancement of cryptographic techniques and provides suitable solutions for secure IoT and blockchain applications. The proposed system achieves a highest improvement rate in key performance parameters over existing methods, resulting in a robust solution for modern cryptography requirements.

E-Voting System Using Blockchain

Abstract: Since the 1970s, electronic voting (e-voting) has evolved, offering efficiency and reduced errors, but faces challenges in security and accessibility. Blockchain technology emerges as a disruptive force with potential to fortify e-voting systems. Voting is fundamental to democracy, yet concerns over reliability and accessibility persist. E-voting, while introduced to address these concerns, remains costly and centralized. Blockchain’s decentralized nature holds promise for overcoming these challenges. This study introduces e-Vote, a blockchain- based voting system designed for privacy, accessibility, and security. Leveraging Ethereum’s blockchain and smart contracts, e-Vote offers a scalable framework for university level elections. Utilizing cryptographic techniques such as homomorphic encryption, e-Vote ensures voter privacy. Our implementation, tested on Ethereum’s Testnet, demonstrates usability, scalability, and efficacy. Successful integration of e-Vote requires two key components: the Election Commission, managing elections and candidates through smart contracts, and the voter’s module, enabling individuals to cast ballots for their respective constituencies, with each ballot recorded on the blockchain to prevent tampering.

Smart and Secure E-Voting Application using Blockchain Technology

The realm of electronic voting systems is experiencing a profound transformation propelled by technological advancements. In response to the demand for secure and transparent voting mechanisms,this project, supervised by Mrs. V.M. Khanapure, endeavors to develop a Smart and Secure E-Voting Application employing blockchain technology. Traditional voting methods often face challenges related to tampering and fraud, which the proposed system aims to mitigate through the decentralized and immutable nature of blockchain. By leveraging the strengths of blockchain, such as transparency and decentralization, the project seeks to establish a trustworthy and efficient electronic voting solution. A primary focus of the Smart and Secure E-Voting Application is the implementation of advanced security measures. Cryptographic techniques are incorporated to secure vote transmissions and protect voter privacy. The application employs end-to-end encryption and multi-factor authentication, fortifyingthe overall security posture of the system. Moreover, the user-friendly interface caters to both voters and election administrators, ensuring accessibility without compromising the high level of security. This project not only addresses current challenges in electronic voting but also lays the foundation for future innovations, setting a precedent for reliable and transparent electoral processes through the utilization of blockchain technology..

Data Transmission Security on Cloud Computing using Algorithm

Cloud computing enables businesses to efficiently store vast amounts of data in the cloud, which can be accessed seamlessly over the Internet without hardware compatibility constraints. However, the transmission of data over the Internet is susceptible to various security threats such as man-in-the-middle attacks, known plaintext attacks, chosen ciphertext attacks, related-key attacks, and pollution attacks. Uploading data to a single cloud service may heighten the risk of compromising confidential information. Prior research has explored several cryptography techniques like SA-EDS, RFDA, and EST to secure data storage across multiple cloud platforms. However, these existing methods exhibit vulnerabilities to various attacks. This article focuses on addressing data security challenges in multi-cloud environments and proposes an advanced approach called Proficient Security over Distributed Storage. PSDS has undergone rigorous testing against multiple attacks, demonstrating its resilience against related-key attacks, pollution attacks, chosen ciphertext attacks, and known plaintext attacks. Additionally, PSDS exhibits reduced computational time compared to existing encryption methods such as STTN and RFD.

Securing Infrared Communication in Nuclear Power Plants: Advanced Encryption for Infrared Sensor Networks.

This study enhances infrared communication security in nuclear power plants' secondary systems, addressing the risk of mechanical and cyber failures. A novel random address generator, employing an innovative S-box, was developed to secure IoT sensor data transmissions to gateway nodes, mitigating eavesdropping, interference, and replay attacks. We introduced a structured IR communication protocol, generating unique, encrypted addresses to prevent unauthorized access. Key-dependent S-boxes, based on a compound chaotic map system, significantly improved encryption, increasing data transmission randomness and uniqueness. Entropy analysis and reduced duplicated addresses confirmed the effectiveness of our method, with the Hash-CCM algorithm showing the highest entropy and fewest duplicates. Integrating advanced cryptographic techniques into IR systems significantly enhances nuclear power plants' security, contributing to the protection of critical infrastructure from cyber threats and ensuring operational integrity.

DETECTING THE SECURITY LEVEL OF VARIOUS CRYPTOSYSTEMS USING MACHINE LEARNING MODELS

In contemporary data management, cloud storage is widely adopted, yet it remains susceptible to security vulnerabilities. Utilizing cryptography techniques is essential for enhancing data security in such environments. One promising approach is hybrid cryptography, which combines multiple algorithms to bolster protection. Our proposed solution integrates RDH with DES algorithms, leveraging their respective strengths to fortify data security before it's stored in the cloud. Extensive testing has validated the efficacy of this approach. Specifically, we introduce an RDH with DES block-based transformation algorithm tailored for image content protection. Notably, our framework enables direct image retrieval and convolution on the content-protected images, enhancing usability alongside security. Keywords: deployment models, Infrastructure as a service, cryptosystems, machine learning

Quantum-Secure Signalling Model for L1/L2 Next-Gen Interconnect and Roaming Networks Over IPX for NB-IoT Traffic: A Review

Signalling security is critical for next-generation mobile (NGN) networks to ensure integrity, privacy, and confidentiality of communication protocols. However, recent research on existing 5G standards has uncovered vulnerabilities that could be exploited by emerging surveillance threats such as HiddenArt and Harvest Now Decrypt Later (HNDTL), as well as the prospect of quantum computing threatening to break classical cryptographic techniques. This systematic literature review (SLR) investigates quantum-based signalling solutions to mitigate these threats in interconnect and roaming 5G networks (IRN) over IP eXchange for Narrowband Internet of Things (NB-IoT) traffic. A database search was conducted, studies were selected and compared based on methods, approaches, techniques, and limitations. The SLR found that quantum key distribution (QKD) combined with quantum teleportation (QT) can protect and mitigate threats and attacks on signalling networks when protocols interact and interoperate in carrier networks. QKD techniques can be used for L1 protocols, while secure direct quantum communication can be used for L2 protocols. This study concludes that further studies are needed to integrate different techniques and protocols for L1/L2 signalling networks to create a robust quantum-secure signalling model for global interconnect and roaming NGN.

Non-Repeating, Normalized Questionnaire Sets Generation and Rendering Based on A Parsed Input Domain with Cryptographically Enforced Secure Access

This project introduces a novel approach to questionnaire generation and rendering, ensuring non repetition, normalization, and secure access control. Traditional methods often suffer from redundancy and lack of security measures, leading to compromised data integrity and confidentiality. Our solution employs a meticulously parsed input domain, where questions are intelligently selected to avoid repetition and maintain a balanced distribution across different domains of inquiry. Furthermore, normalization techniques are applied to standardize the questionnaire structure, enhancing comparability and analysis.To ensure data security and privacy, the system leverages cryptographic techniques to enforce secure access controls. This includes robust authentication mechanisms, data encryption, and integrity verification, safeguarding sensitive information throughout the questionnaire lifecycle.The implementation of this project not only improves the efficiency and reliability of questionnaire generation but also addresses critical concerns regarding data security and privacy. This approach has significant implications across various domains, including research, healthcare, and customer feedback systems, where accurate data collection and confidentiality are paramount