Encrypted Data Research Articles

StegaCrypt Integrating Hybrid Cryptography and Image Steganography

This paper discusses the combination of cryptography and steganography for digital data security. Cryptography and steganography are two of the most significant techniques in information security, to be used differently: data is secured against unauthorized use and ensured integrity by way of transforming it into some unreadable format, whilst steganography hides a file inside another file. The project aim is to synthesize the techniques for a robust system to protect secure communication and data. We use RSA cryptography for the encryption, which is the asymmetric algorithm of high-level security, and image-based steganography to place the encrypted data within a digital image. The processes of cryptography will ensure decrypting the data only in the hands of authorized recipient parties, and steganography provides additional protection on the data invisibility front. This project shows that this integration of two technologies will safeguard sensitive information from all types of threats. In view of various methods to combine cryptography and steganography, this project puts forward the significance of incorporating multi-layer security to combat different types of cyber-attacks. The implementation also includes an elaborated coding using algorithms and development of a GUI to facilitate user interaction with the system. This further ensures that it is not only technically sound but also user-friendly. The last system is to send confidential data toward the broader field of secure communications and information protection, efficiently, securely, and in a user-friendly way

Secured Folder Encryption and Decryption

In today's digital world, when sensitive data is routinely targeted by thieves, strong encryption technologies are critical for preventing breaches and ransomware attacks. Our research solves this need by creating a Secure Folder Encryption System that employs AES-GCM (Advanced Encryption Standard - Galois/Counter Mode) and HMAC (Hash-based Message Authentication Code) to secure data confidentiality and integrity when stored on cloud servers. Drawing on research on ECG image file encryption using ECDH and AES-GCM, we choose AES-GCM for its high resistance to manipulation and unauthorized access, while ECDSA (Elliptic Curve Digital Signature Algorithm) improves data authenticity. This method encrypts files before uploading them to the cloud, data is secure even if intercepted. In addition to a user friendly interface, the system is tuned for performance, evaluating encryption and decryption times and file sizes. Given the concerning trend of stolen encrypted data being sold on the dark web, as reported in recent headlines, our project offers a secure and effective method for safeguarding critical information in cloud storage

Appropriately matching transport care units to patients in an interhospital Transport Care: an implementation report.

In interfacility transport care, a critical challenge exists in accurately matching ambulance response levels to patients' needs, often hindered by limited access to essential patient data at the time of transport requests. Existing systems cannot integrate patient data from sending hospitals' electronic health records (EHR) into the transfer request process, primarily due to privacy concerns, interoperability challenges, and the sensitive nature of EHR data. We introduce a distributed digital health platform, Interfacility Transport Care (ITC)-InfoChain, designed to solve this problem without compromising EHR security or data privacy. This report details the implementation of Interfacility Transport Care (ITC)-InfoChain, a secure, blockchain-based platform designed to enhance real-time data sharing without compromising data privacy or EHR security. The ITC-InfoChain platform prototype was implemented on AWS cloud infrastructure, employing Hyperledger Fabric as a permissioned blockchain. Key elements included participant registration, identity management, and patient data collection isolated from the sending hospital's EHR system. The client program submits encrypted patient data to a distributed ledger, accessible to the receiving facility's critical care unit at the time of transport request and EMS teams during transport through the PatienTrack web application. Performance was evaluated through KPIs such as data transaction times and scalability across transaction loads. The ITC-InfoChain demonstrated strong performance and scalability. Data transaction times averaged 3.1 seconds for smaller volumes (1-20 transactions) and 6.4 seconds for 100 transactions. Optimized configurations improved processing times to 1.8-1.9 seconds for 400 transactions. These results confirm the platform's capacity to handle high transaction volumes, supporting timely, real-time data access for decision-making during transport requests and patient transfers. The ITC-InfoChain platform addresses the challenge of matching appropriate transport units to patient needs by ensuring data privacy, integrity, and real-time data sharing, enhancing coordination of patient care. The platform's success suggests potential for regional pilots and broader adoption in secure healthcare systems. Stakeholder resistance due to blockchain unfamiliarity and data privacy concerns remains. Funding has been sought to support a pilot program to address these challenges through targeted education and engagement.

Optically Transparent Complex-Amplitude Metasurface for Full-Space Manipulation of Frequency-Multiplexed Holographic Imaging.

Restricted by the physical properties of materials, most traditional metasurfaces (MSs) cannot achieve transparent stealth in the visible spectrum. Although some metasurfaces for holography have been designed, there is no standard method for evaluating the advancement of wavefront manipulation under different design algorithms. Here, a complex-amplitude metasurface with optical transparency (OT) and full-space manipulation is presented in the millimeterwave band. Through frequency multiplexing, two holographic images "HOLO" and "GRAM" with high quality are designed, achieving the signal-to-noise ratios of 14.6 and 14.4 dB, respectively. The 64 × 64 bilayered metasurface consists of glass and poly(ethylene terephthalate) (PET) substrates and three metallic mesh layers. By change of the opening size and oriented angle of split rings, phase and amplitude modulations are independently realized. Besides, a new method is provided to assess the superiority of the metasurface holography system for near-field energy regulation. The prototype was fabricated using micro-nano-technology and tested. Two holographic images were experimentally demonstrated and show good agreement with theoretical calculations and simulated results. With the advantages of OT and full-space complex-amplitude customization, the proposed metasurface holograms for frequency-multiplexed may find prospective application aspects in target perception, multichannel data storage and encryption, and many other related fields.

Pbins: private bins for top-k semantic search over encrypted data using transformers

pbins: private bins for top-k semantic search over encrypted data using transformers

Crypto-Spatial: A New Direction in Geospatial Data

Abstract. Geospatial technology involves the use of maps, satellite imagery, and spatial databases to collect, analyze, and visualize geographic data. Understanding the link between blockchain and geospatial technology is crucial as it opens the door to innovation and progress in both fields. “The use of Blockchain as a system for managing and verifying geospatial data necessitates accuracy, integrity, and trustworthiness”(Pengxiang et al 2022). Improving data integrity, promoting collaboration, and revolutionizing applications in the geospatial domain are all possible through the combination of these two technologies. The World Wide Web (WWW) has expanded connections worldwide, but it also makes networks susceptible to multiple attacks from various anonymous sources. Packets of fixed or variable sizes are used for transmitting data across nodes. Data is encrypted using secure algorithms at the application level, then packetized and sent at lower levels in the OSI architecture. The encrypted data can be obtained by an intruder by organizing the data contents of each packet once they have access to all the packets. Intruders may also attempt to break the secured algorithm used by the sender. This sophisticated approach to Data Security is the content and order of data, ensuring that unauthorized individuals are unable to discern sensitive information during data transmission. The primary objective of this paper is to introduce a novel spatial encryption technique designed to enhance the security of data transfers within networks. This paper, founded on a comprehensive literature review, endeavors to propose a research project focused on exploring futuristic solutions, particularly within the post-quantum era.

Real-Time Data Governance and Compliance in Cloud-Native Robotics Systems

This study investigates the frameworks and challenges of real-time data governance and compliance in cloud-native robotics systems, focusing on data integrity, cloud security, regulatory adherence, and cybersecurity risks. Using extensive datasets from the Amazon AWS Open Data Registry, the EU GDPR Enforcement Tracker, and Kaggle’s IoT dataset, the analysis explores cloud-native systems' data accuracy, security, and governance. Data were extracted through a standardized process: performance metrics, including latency and error rates, were gathered from Amazon AWS to assess system efficiency, GDPR violation records were analyzed from the EU Enforcement Tracker to understand compliance trends, and data volume and governance metrics from Kaggle’s IoT dataset were correlated to identify governance challenges. Together, these data sources provide comprehensive insights into how cloud-native systems can be optimized for real-time operations. The study highlights the cloud security benefits and governance advantages inherent to cloud-native frameworks, such as real-time monitoring, automated threat detection, and data encryption, which collectively reduce unauthorized access risks while supporting operational efficiency. Findings indicate high data accuracy (0.51% error rate) and low latency (mean of 48.96 ms) across systems; however, processing time variability (standard deviation of 28.61 ms) signals a need for further optimization in time-sensitive environments. The regression analysis of GDPR violations reveals a substantial penalty increase of €53,789.41 per violation, emphasizing the financial risks of non-compliance. Correlation analysis (r = 0.083 for data volume and governance failures) suggests that external cybersecurity threats have a greater impact on governance than internal metrics, underscoring the importance of adaptive governance frameworks that support both data integrity and regulatory compliance in cloud-native robotics systems.

Exploration of Reliability and Security of E- Banking: Selected Urban Cooperative banks

The rise of E-banking has introduced unprecedented convenience and accessibility for users, transforming traditional banking operations into efficient online experiences. However, this shift brings challenges, particularly in terms of security and reliability, as users now expect robust protections for their financial data and consistent, dependable service. This study investigates the perceptions of security and reliability in digital banking among 290 urban cooperative banking customers. Using factor analysis, we explore key dimensions of e-banking services, focusing on elements like encryption, biometric security, and trust in transaction reliability. The analysis identified two primary factors: security and reliability, each with distinct contributions to overall user satisfaction. The factor loadings revealed that users had mixed confidence in encryption technologies and data privacy measures, with some expressing high levels of satisfaction, while others indicated significant concerns. Additionally, reliability emerged as a critical factor, with findings indicating that a notable portion of customers were dissatisfied with the timeliness and trustworthiness of e-banking services. For data collection, a structured survey measured responses on a five-point Likert scale, assessing variables such as trustworthiness, encryption effectiveness, privacy maintenance, and personal security practices. Through the use of descriptive and factor analysis, we quantified customer sentiments and identified patterns in user satisfaction. The results suggest that while there is moderate confidence in digital banking security, the reliability of service delivery remains an area for improvement. Banks should consider addressing these findings by enhancing transparency in their security protocols and strengthening service consistency to better meet user expectations in digital banking environments.

Triplet Energy Gap‐Regulated Room Temperature Phosphorescence in Host–Guest Doped Systems

AbstractThe organic room temperature phosphorescence (RTP) materials via host–guest doped method receive considerable attention in the fields of optoelectronics, bioimaging, and information encryption. Despite many host–guest doped materials with excellent RTP properties have been developed, their luminous mechanism is still limited. Here, a series of host–guest doped materials, using benzophenone as the host and quinone compounds as the guests, were constructed to investigate the effect of the triplet energy gap (ΔET) between the host and guest on triplet states population. The guest's triplet state is proposed to be a “triplet energy reservoir”, gathering the triplet excitons to emit RTP when ΔET is large and returning triplet excitons to the host when ΔET is small. By combining the results of steady‐state and delayed emission spectra, time‐resolved transient absorption spectra, and theoretical calculations, a bidirectional energy transfer process is proved, which are triplet‐triplet energy transfer and reverse triplet‐triplet energy transfer processes. The thermal equilibrium of these two energy transfer processes can be regulated by the ΔET and temperature. The potential applications of these RTP properties are also realized in data encryption and anti‐counterfeiting. This work provides valuable insight into the design of host–guest doped materials based on energy transfer mechanisms.

Triplet Energy Gap-Regulated Room Temperature Phosphorescence in Host-Guest Doped Systems.

The organic room temperature phosphorescence (RTP) materials via host-guest doped method receive considerable attention in the fields of optoelectronics, bioimaging, and information encryption. Despite many host-guest doped materials with excellent RTP properties have been developed, their luminous mechanism is still limited. Here, a series of host-guest doped materials, using benzophenone as the host and quinone compounds as the guests, were constructed to investigate the effect of the triplet energy gap (ΔET) between the host and guest on triplet states population. The guest's triplet state is proposed to be a "triplet energy reservoir", gathering the triplet excitons to emit RTP when ΔET is large and returning triplet excitons to the host when ΔET is small. By combining the results of steady-state and delayed emission spectra, time-resolved transient absorption spectra, and theoretical calculations, a bidirectional energy transfer process is proved, which are triplet-triplet energy transfer and reverse triplet-triplet energy transfer processes. The thermal equilibrium of these two energy transfer processes can be regulated by the ΔET and temperature. The potential applications of these RTP properties are also realized in data encryption and anti-counterfeiting. This work provides valuable insight into the design of host-guest doped materials based on energy transfer mechanisms.

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.

Privacy-preserving local clustering coefficient query on structured encrypted graphs

Graphs and graph databases serve as the fundamental building blocks for various network structures. In real-world network scenarios, nodes often aggregate due to their approximate organizational associations with each other. The local clustering coefficient, which evaluates the proximity of nodes within a graph, plays an important role in quantifying the structural properties of graphs in scrutinizing network robustness and understanding its intricate dynamics. Despite the growing popularity of easily accessible cloud services among small and medium-sized enterprises as well as individuals, the potential risk of data privacy disclosure when outsourcing large graphs to third-party servers is increasing. It is vital to explore a technique for executing queries on encrypted graph data. In this paper, we propose a structured encryption scheme to achieve privacy-preserving local clustering coefficient query (STE-CC) on the outsourced encrypted graphs. To calculate the clustering coefficient, we design the PSIsum protocol to sum the number of intersections, in which the basic private set intersection (PSI) protocol combines Bloom filter (BF) and garbled Bloom filter (GBF) to perform the private matching for counting the number of common neighbors. When configured with appropriate parameters, it can achieve no false negatives and negligible false positives. Finally, the security analysis and experimental evaluation on real-world graph data substantiate the effectiveness and efficiency of our approach.

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.