Encrypted Data Research Articles

Pathway-directed recyclable chirality inversion of coordinated supramolecular polymers

It remains challenging to elucidate the fundamental mechanisms behind the dynamic chirality inversion of supramolecular assemblies with pathway complexity. Herein, metal coordination driven assembly systems based on pyridyl-conjugated cholesterol (PVPCC) and metal ions (Ag+ or Al3+) are established to demonstrate pathway-directed, recyclable chirality inversion and assembly polymorphism. In the Ag(I)/PVPCC system, a competitive pathway leads Ag-Complex to form either kinetically controlled supramolecular polymer (Ag-SP I) or thermodynamically favored Ag-SP II, accompanied by reversible chiroptical inversion. Conversely, the Al(III)/PVPCC system displays a solvent-assisted consecutive pathway: the Al-Complex initially forms ethanol-containing Al-SP II, and subsequently converts into ethanol-free Al-SP I with opposite chiroptical performance upon thermal treatment. Moreover, stable chirality inversion in the solid state enables potential dynamic circularly polarized luminescence encryption when Ag(I)/PVPCC is co-assembled with thioflavin T. These findings provide the guidance for the dynamic modulation of chirality functionality in supramolecular materials for applications in information processing, data encryption, and chiral spintronics.

Building an Impenetrable Vault: Advanced Cybersecurity Strategies for Database Servers

Organizations are relying more on database servers as storage for important information. This makes it imperative for strong measures against cyber-attack to safeguard sensitive data. The study focusses on some advanced ways of protecting database servers from cyber-crime and these are: SQL injection prevention, role-based access control, data encryption techniques, and insider threat mitigation. It also looks into the latest developments in the area such as machine learning based abnormality detection, Zero Trust architecture, and multi-factor authentication. Such practices make it possible to establish a multi-layered defensive mechanism that enhances data protection by considering the outside as well as inside dangers. Besides compliance with regulations, maintaining visibility and patch management is vital so as to remain ahead of changing cyberspace dangers. Therefore, this research presents an array of recommendations to address the entire security landscape for resilient database platforms against sophisticated assaults. The conclusions point out that there is need to amalgamate these practices towards ensuring safety in terms of risks related with contemporary database server setups. Key Words: Database Security, SQL Injection, Role-Based Access Control, Data Encryption, Insider Threat, Zero Trust Architecture.

Enhanced Asymmetric Circularly Polarized Luminescence in Self-Organized Helical Superstructures Enabled by Macro-Chiral Liquid Crystal Quantum Dots.

Circularly polarized luminescent (CPL) materials have garnered considerable interest for a variety of advanced optical applications including 3D imaging, data encryption, and asymmetric catalysis. However, the development of high-performance CPL has been hindered by the absence of simple synthetic methods for chiral luminescent emitters that exhibit both high quantum yields and dissymmetry factors. In this study, we present an innovative approach for the synthesis of macro-chiral liquid crystal quantum dots (Ch-QDs/LC) and their CPL performance enhancement through doping with 4-cyano-4'-pentylbiphenyl (5CB), thus yielding a CPL-emitting generator (CEG). The Ch-QDs/LCs were synthesized, and their surfaces functionalized with a chiral mesogenic ligand, specifically cholesteryl benzoate, anchored via a lipoic acid linker. Under the regulation of chiral 2S-Zn2+ coordination complexes, the chiral LC encapsulation process promotes coordinated ligand substitution, resulting in an exceptional quantum yield of 56.3%. This is accompanied by high absorption dissymmetry factor (gabs) and luminescence dissymmetry factor (glum) values ranging from 10-3 to 10-2, surpassing most reported dissymmetry factors by at least an order of magnitude. The modular Ch-QDs/LCs demonstrate the ability to transfer chirality to the surrounding medium efficiently and manifest macro-chiral characteristics within a nematic LC matrix. Utilizing Ch-QDs/LC as an effective CPL emitter within achiral 5CB matrices enabled the system to achieve a maximum glum value of 0.35. The resultant CEG device acted as a direct CPL source, initiating enantioselective photopolymerization.

A Deep Cryptographic Framework for Securing the Healthcare Network from Penetration

Ensuring the security of picture data on a network presents considerable difficulties because of the requirement for conventional embedding systems, which ultimately leads to subpar performance. It poses a risk of unauthorized data acquisition and misuse. Moreover, the previous image security-based techniques faced several challenges, including high execution times. As a result, a novel framework called Graph Convolutional-Based Twofish Security (GCbTS) was introduced to secure the images used in healthcare. The medical data are gathered from the Kaggle site and included in the proposed architecture. Preprocessing is performed on the data inserted to remove noise, and the hash 1 value is computed. Using the generated key, these separated images are put through the encryption process to encrypt what they contain. Additionally, to verify the user’s identity, the encrypted data calculates the hash 2 values contrasted alongside the hash 1 value. Following completion of the verification procedure, the data are restored to their original condition and made accessible to authorized individuals by decrypting them with the collective key. Additionally, to determine the effectiveness, the calculated results of the suggested model are connected to the operational copy, which depends on picture privacy.

DSDOS Cloud: A Decentralized Secure Data Outsourcing System With Hybrid Encryption, Blockchain Smart Contract‐Based Access Control, and Hash Authentication Codes for Cloud Security

ABSTRACTWith the increasing trend of outsourcing data to cloud services, ensuring data security and privacy has become crucial. Typically, data are stored on cloud servers in encrypted form to mitigate risks. However, accessing the encrypted data requires an access key distributed by a third party. If this third party is untrustworthy, it poses a significant security threat to the system. To address this challenge, we propose a Decentralized Secure Data Outsourcing System (DSDOS) that uses blockchain technology to ensure data security and privacy. The DSDOS system comprises three modules: data security and privacy, access control and authorization, and data integrity and availability. The data security and privacy module uses a hybrid encryption scheme that combines Advanced Encryption Standard (AES), partially homomorphic encryption (PHE), and Diffie–Hellman (DH) to ensure secure data storage and access. The access control and authorization module uses a blockchain‐based smart contract system to manage access to the encrypted data. The data integrity and availability module uses hash‐based message authentication code (HMAC) to ensure that the data are not tampered with and is always available. We conducted a security and performance analysis of the DSDOS system and found that it outperforms previous schemes in terms of security and performance. The DSDOS system is a secure and privacy‐preserving data outsourcing system that can be used to mitigate the security risks associated with traditional cloud storage systems.

Context-Aware Adaptive Encryption: Integrating Sensitive Data Detection and Network intrusion detection for Dynamic Data Security and Encryption

In today's digital landscape, ensuring the security of sensitive data and protecting against network intrusions are critical challenges. This research project develops and evaluates a novel context-aware adaptive encryption system that integrates sensitive data detection, network intrusion detection, and dynamic encryption techniques to enhance data security. The proposed system employs deep learning models to identify sensitive information and machine learning algorithms to monitor network activity for potential intrusions. Upon detecting sensitive data or a security threat, the system automatically applies encryption with adjustable strength based on the context, increasing protection in high-risk situations. This approach minimizes unnecessary overhead in low-risk scenarios while maintaining robust security measures. Through simulations using real-world data, the system's effectiveness in accurately detecting sensitive information and network intrusions, as well as its capability to adapt encryption dynamically, is evaluated. The results demonstrate the potential of combining machine learning with adaptive security measures to create a responsive and efficient data protection system.

Capabilities of cellebrite universal forensics extraction device in mobile device forensics

The powerful digital forensics tool cellebrite universal forensics extraction device (UFED) extracts and analyzes mobile device data, helping investigators solve criminal and cybersecurity cases. Advanced methods and algorithms allow Cellebrite UFED to recover data from erased or obscured devices. Cellebrite UFED can pull data from call logs, texts, emails, and social media, providing valuable evidence for investigations. The use of smartphones and tablets in personal and professional settings has spurred the development of mobile device forensics. The intuitive user interface speeds up data extraction and analysis, revealing crucial information. It can decrypt encrypted data, recover deleted files, and extract data from multiple devices. The sector's best data extraction functionality, Cellebrite UFED, helps forensic analysts gather crucial evidence for investigations. Legal and ethical considerations are crucial in mobile device forensics. Legal considerations include allowing access to data, protecting privacy, and adhering to chain of custody protocols. Ethics include transparency, defamation, and information exploitation protection. Using Cellebrite UFED, researchers can navigate complex data on mobile devices more efficiently and precisely. Artificial intelligence (AI) and machine learning (ML) algorithms may automate data extraction in future tools. Examiners must train, maintain, and establish clear protocols for using Cellebrite UFED in forensic investigations.

SERAV Deep-MAD: Deep Learning-Based Security–Reliability–Availability Aware Multiple D2D Environment

The tremendous growth of internet-connected devices can lead to network congestion, making it essential to incorporate new technologies to optimize performance. Thus, Device to Device(D2D) communication has been deemed as an emerging technology that can be used to communicate efficiently. However, establishing a secure and reliable mechanism for Device-to-Device (D2D) communication that ensures security, reliability, and availability poses a significant challenge. To tackle these issues a novel deep learning-based security-reliability-availability aware multiple D2D environment (SERAV Deep-MAD) has been proposed for Secure D-2-D Communication in a Fog environment. The proposed method utilizes a Fully Homomorphic Quantum Diffie Hellman Encryption (FHQDHE) to secure the data while transmitting. The proposed method utilizes the novel secure Quantum Diffie Hellman key exchange (QDHKE) technique for sharing the key to transform the plain data into cipher data, then applies FH operations on the encrypted data before transferring it to the Fog environment. Whenever a client requests data from the Fog, the Fog provider verifies the client's access rights. Moreover, the Attention-based Bi-GRU model is utilized to categorize whether the device is non-attack or attack, if the data is attacked then the proposed model classifies multiple attacks. The NS2 Simulator is used to verify and validate the proposed SERAV Deep-MAD model, and resiliency analysis is done to assess performance. The proposed techniques attain a higher accuracy of 98.18% which is 1.58%, 2.02%, and 2.41% better than MECC, MIMO, andAAKA-D2D respectively.

Improving Data Security in Cloud Computing through the Implementation of a Hybrid Encryption Algorithm

As cloud computing continues to gain popularity, the security of sensitive data stored in the cloud remains a paramount concern for organizations and individuals alike. Traditional encryption methods, while effective, often struggle to balance security, efficiency, and usability. This paper presents a hybrid encryption algorithm that combines the strengths of symmetric and asymmetric encryption to enhance data security in cloud computing environments. The proposed hybrid approach leverages symmetric encryption for fast and efficient data encryption, while asymmetric encryption is utilized for secure key exchange. By implementing this dual-layer encryption strategy, we can significantly improve data confidentiality and integrity during transmission and storage. Additionally, the algorithm incorporates robust key management practices, ensuring that encryption keys are safeguarded against unauthorized access. Through a series of performance evaluations and security assessments, we demonstrate that our hybrid encryption algorithm not only provides superior security but also maintains acceptable levels of performance, making it suitable for real-time applications. The results indicate that this approach effectively mitigates risks associated with data breaches and unauthorized access, positioning it as a viable solution for enhancing data security in cloud computing. This research contributes to the ongoing discourse on cloud security, offering a practical framework for organizations to protect their sensitive information in increasingly complex digital landscapes.

Methods of Extracting and Analyzing Metadata for Evidentiary Purposes

This paper examines methods for extracting and analyzing metadata for evidentiary purposes in civil proceedings. Through a comprehensive review of current literature, legal cases, and forensic techniques, it explores the diverse approaches to metadata analysis across various digital domains, including file systems, emails, documents, web browsers, mobile devices, cloud storage, social media, and emerging technologies. The study highlights the critical role of metadata in establishing the authenticity, reliability, and chronology of digital evidence. It also addresses the challenges posed by encrypted data, large-scale analysis, and the need for robust quality assurance processes. The findings underscore the importance of adapting forensic methodologies to evolving digital landscapes while maintaining legal and ethical standards. This research contributes to the ongoing development of best practices in digital forensics and their application in civil litigation.

Privacy preserving verifiable federated learning scheme using blockchain and homomorphic encryption

This paper introduces a novel Privacy-Preserving Verifiable Federated Learning (PPVFL) scheme that integrates blockchain technology and homomorphic encryption to address critical challenges in decentralized machine learning. The proposed scheme ensures data privacy, integrity, verifiability, robust security, and efficiency in collaborative learning environments, particularly in sensitive domains such as healthcare. By leveraging blockchain’s decentralized, immutable ledger and homomorphic encryption’s capability to perform computations on encrypted data, the model maintains the confidentiality of sensitive information throughout the learning process. The inclusion of Byzantine fault tolerance and Elliptic Curve Digital Signature Algorithm (ECDSA) further enhances the system’s security against malicious attacks and data tampering, while the optimization of computational processes ensures efficient model training and communication. The novelty of this work lies in the seamless integration of blockchain and homomorphic encryption within a federated learning framework, specifically tailored for post-quantum cryptography, a combination that has not been extensively explored in prior research. This research represents a significant advancement in secure and efficient federated learning, offering a promising solution for industries that prioritize data privacy, security, and trust in collaborative machine learning. The effectiveness, security, and efficiency of the PPVFL scheme were validated using the Glaucoma dataset. The proposed method outperformed baseline federated learning algorithms, achieving a Dice coefficient of 0.918 and a Hausdorff distance of 4.05 on Severe Glaucoma (SG) cases, compared to 0.905 and 5.27, respectively, with traditional FedAvg. Moreover, the integration of blockchain and homomorphic encryption ensured that data privacy was upheld without compromising model performance, while efficient computation and communication processes minimized latency and resource consumption. This study contributes a robust, privacy-preserving, secure, efficient, and verifiable federated learning framework that addresses the pressing need for secure and scalable data management in distributed machine learning environments.

A Blockchain and Zero Knowledge Proof Based Data Security Transaction Method in Distributed Computing

In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. In order to address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we devise attribute atomic matching smart contracts based on precise data property alignment, with the objective of achieving fine-grained matching of data attributes between trading parties. In the trading execution phase, lightweight cryptographic algorithms based on elliptic curve cryptography (ECC) and non-interactive zero-knowledge proofs are employed for the dual encryption of trading data and the generation of attribute proof contracts, thus ensuring the security and privacy of the data. The results of experiments conducted on the Ethereum platform in an industrial IoT scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection.

Image Encryption Using High-Speed Scrambling and ‎Modular Arithmetic

With the enormous growth and advancement of the computer industry, it is critically necessary to transmit a large amount of encrypted multimedia data to prevent third parties from attacking one's privacy or misbehaving. This study proposes a new encryption strategy for preserving original images. It is highly effective and has been shown to be resilient against impulsive noise and loss of data. At first, some random data is entered within the perimeter of the image. Then, three rounds of high-speed scrambling and adaptive diffusion for pixels are carried out to mix the contiguous pixels at random and distribute the newly placed data throughout the image. The suggested encryption method can be used to encrypt original images in any form of representation directly. We offer a particular operation to carry out adaptive diffusion for pixel modulo arithmetic. The results of encryption are evaluated using the histogram, which shows a near-zero correlation with a Number of Pixel Change Rate (NPCR) of 099.624, a Unified Average Change Intensity (UACI) of 033.577, and an average entropy of 7.9993. The proposed method can accomplish much better speed and adapt better to impulse noise and loss of data interference than several conventional and cutting-edge encryption methods.

A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System

Due to the expansion of Artificial Intelligence (AI), especially Machine Learning (ML), it is more common to face confidentiality regulations about using sensitive data in learning models generally hosted in cloud environments. Confidentiality regulations such as HIPAA and GDPR seek to guarantee the confidentiality and privacy of personal information. Input and output data of a learning model may include sensitive data that must be protected. Adversaries could intercept and exploit this data to infer more sensitive data or even to determine the structure of the prediction model. To guarantee data privacy, one option could be encrypting data and making inferences over encrypted data. This strategy would be challenging for learning models that now must receive encrypted data, make inferences over encrypted data, and deliver encrypted data. To address this issue, this paper presents a privacy-preserving machine learning approach using Fully Homomorphic Encryption (FHE) for a model that predicts risk levels of suffering a traffic accident. Despite the limitations of experimenting with FHE on machine learning models using a low-performance computer, limitations that are undoubtedly overcome by using high-performance computational infrastructure, we built some encrypted models. Among the encrypted models based on Decision Trees, Random Forests, XGBoost, and Fully Connected Neural Networks (FCNN), the model based on FCNN reached the highest accuracy (80.1%) for the lowest inference time (8.476 s).

Research and Design of Data Encryption Circuit based on D Flip-Flop

Since information technology is developing so quickly, the problem of data security has become increasingly prominent. Data encryption technology, as an important means of securing data transmission, plays a vital role in resisting network attacks and protecting privacy. In this paper, a data encryption circuit based on D flip-flop is studied, aiming to improve the efficiency and security of data encryption. First, this paper introduces the basic principles of data encryption and analyzes the application of D flip-flop in encryption circuit. Subsequently, the design principle of Moore’s state machine is elaborated, and encryption circuit is designed with the use of the state graph and state table. Further, this paper demonstrates the logic circuit design process., including the derivation of D-flip-flop input Circuit construction and Boolean expressions. This research provides a hardware implementation design solution for the design of data encryption circuits, which is expected to play a role in improving encryption speed and enhancing security.

An optimized dynamic attribute-based searchable encryption scheme.

Cloud computing liberates enterprises and organizations from expensive data centers and complex IT infrastructures by offering the on-demand availability of vast storage and computing power over the internet. Among the many service models in practice, the public cloud for its operation cost saving, flexibility, and better customer support popularity in individuals and organizations. Nonetheless, this shift in the trusted domain from the concerned users to the third-party service providers pops up many privacy and security concerns. These concerns hindrance the wide adaptation for many of its potential applications. Furthermore, classical encryption techniques render the encrypted data useless for many of its valuable operations. The combined concept of attribute-based encryption (ABE) and searchable encryption (SE), commonly known as attribute-based keyword searching (ABKS), emerges as a promising technology for these concerns. However, most of the contemporary ABE-based keyword searching schemes incorporate costly pairing and computationally heavy secret sharing mechanisms for its realization. Our proposed scheme avoids the expensive bilinear pairing operation during the searching operation and costly Lagrange interpolation for secret reconstruction. Besides, our proposed scheme enables the updation of access control policy without entirely re-encrypting the ciphertext. The security of our scheme in the selective-set model is proved under the Decisional Bilinear Diffie-Hellmen (DBDH) assumption and collision-free. Finally, the experimental results and performance evaluation demonstrate its communication and overall efficiency.

An Efficient Framework for Secure Dynamic Skyline Query Processing in the Cloud

AbstractThis study introduces an innovative framework named scale for processing dynamic skyline queries securely in cloud environments. Unlike previous approaches that require complex operations on encrypted data, scale simplifies dynamic skyline domination to mere comparisons, significantly improving query efficiency. Through empirical evaluations over four datasets, we show that scale accelerates query processing nearly 1000-fold compared to existing state-of-the-art methods. Specifically, scale shows significant efficiency improvements by simplifying query interactions to a single round between the user and the cloud, which is validated through empirical studies on multiple datasets. Moreover, we introduce two distributed versions of scale, dist-scale-s and dist-scale-e, which further optimize performance by facilitating parallel processing. This adaptation showcases a substantial reduction in response times and computational overhead, underpinning the scalability and effectiveness of our framework in handling large-scale, secure cloud-based queries.

Decentralized CP-ABE Scheme for Enhanced University Data Security Using Blockchain

In studying ample data security in universities, it is essential to protect the privacy and integrity of data. Current schemes utilize blockchain technology for data sharing, where cloud servers are responsible for storing ciphertext and performing partial encryption and decryption calculations. Data encryption technology can effectively prevent hacker attacks and ensure the integrity of data transmission. However, cloud servers that are only partially trusted may tamper with the ciphertext or return incorrect results. To solve these problems, this paper improves the privacy of access policy, attribute revocation and data authenticity of traditional CP-ABE algorithm. A protection scheme for ample public data security in universities is proposed, and a decentralized network structure based on multiple nodes is designed to improve the efficiency of data queries. The simulation results show that the scheme performs well in the scenario where the number of system attributes is moderate and the complexity of the access policy is not high.

Overview of homomorphic encryption technology for data privacy

This study examines the overview of homomorphic encryption technology for data privacy. In the era of big data, the growing need to utilize vast amounts of information while ensuring privacy and security has become a significant challenge. Homomorphic encryption technology has gained attention as a solution for privacy-preserving data processing, allowing computations on encrypted data without exposing sensitive information. This study introduces the concept of data privacy preservation and explores the evaluation of homomorphic encrypted technology. The focus is on analyzing both partial and full homomorphic encryption methods, highlighting their respective characteristics, evaluation criteria, and the current state of research. Partial homomorphic encryption supports limited operations, while full homomorphic encryption enables unlimited computation on encrypted data, though both face challenges related to computational overhead and efficiency. Additionally, this paper addresses the ongoing issues and limitations associated with homomorphic encryption, such as its complexity, large encryption volumes, and difficulties in handling large-scale datasets. Despite these challenges, researchers continue to refine the technology and expand its applications in cloud computing, big data analytics, and privacy-preserving computing environments. This study also discussed potential future research avenues aimed at improving the scalability, efficiency, and security of homomorphic encryption to support broader, real-world applications. Ultimately, homomorphic encryption is positioned as a key enabler for secure data utilization in an increasingly privacy-conscious digital landscape.

Secure authentication and encryption via diffraction imaging-based encoding and vector decomposition

Abstract Traditional optical encryption systems have security risks due to their linearity and usually encounter problems such as the heavy burden of key transmission and storage. This paper proposes a novel security-enhanced optical image authentication and encryption framework that combines diffractive imaging-based encryption with the vector decomposition algorithm (VDA). Chaotic random phase masks (CRPMs) are used to encrypt data for authentication via VDA, and a pair of complementary binary matrix keys are utilized to extract information from the encrypted data to generate ciphertext. During the authentication and decryption processes, a sparse reference image is reconstructed from the ciphertext for verification. If the authentication is successful, image decryption can be executed using a key-assisted phase retrieval algorithm. The employment of nonlinear VDA, an additional layer of authentication, and the use of CRPMs and binary matrix keys enhance security and address key burden concerns. Simulation results demonstrate the feasibility, effectiveness, and security of the scheme.