Encrypted Data Research Articles

Structural Rigidification Strategy Based on Self-Assembly Enabled Reversible Excited-State Conversion of Iridium(III) Complexes for Multiple-Stimulus-Responsive Data Encryption.

Stimulus-responsive chromic materials exhibit color-switching properties under specific external stimuli and have been widely used in various fields. Transition-metal complexes show great potential applications as promising candidates for stimulus-responsive chromic materials, as their excited states not only depend on the chemical composition but are also affected by the intermolecular stacking modes. Owing to the intrinsic difficulty in the ordered stacking of the octahedral configuration, changing the stacking modes of iridium(III) complexes for multiple-stimulus responsiveness remains a significant challenge. In this work, we propose a structural rigidification strategy based on self-assembly to reversibly regulate the excited states of iridium(III) complexes, therefore achieving color switch under different stimulus conditions. We prepare cationic iridium(III) complexes by using tetrakis(perfluorophenyl)-borate ([B(PhF5)4]-) as the counterion, whose matching tetrahedral configuration and electron-deficient aromaticity enables polar-π interaction with the octahedral iridium(III) cations, inducing self-assembly to form structural rigidification. The structural rigidity restricts the large conformational changes of the metal-to-ligand charge transfer (3MLCT) excited state, and facilitates the conversion from the 3MLCT to the ligand-center (3LC) excited state in aggregated states. The excited-state conversion results in a 54 nm blue shift (from yellow to sky blue) in the photoluminescence spectra. As a result, we report a series of cationic iridium(III) complexes with different responses to low temperature, vapor fuming, and mechanical force, therefore achieving multiple-stimulus-responsive data encryption. Our work provides a novel strategy to achieve ordered stacking of octahedral complexes, shows a deeper understanding of the photophysical processes of transition-metal complexes, and offers a new perspective to develop multiple-stimulus-responsive chromic materials.

An Efficient Multilayer approach for Securing E-Healthcare Data in Cloud using Crypto – Stego Technique

Healthcare data has been moving to cloud platforms in recent years, which has increased accessibility and scalability but also raised security issues. Ensuring data integrity and safeguarding private health information from unwanted access are critical. This paper presents a comprehensive strategy to integrate effective Elliptic Curve Cryptography ECC-AES with steganography techniques to improve the security of healthcare data in the cloud. ECCAES is especially well-suited for cloud situations with limited resources since it provides strong security with reduced key sizes. Confidentiality is guaranteed by encrypting healthcare data using ECC- AES before storage, reducing the possibility of data breaches. Steganography techniques are also integrated to improve security against skilled adversaries by adding an extra degree of obfuscation by concealing encrypted data inside innocuous files or images. Strict key management procedures, access control systems, and frequent security audits are important components of the proposed system that ensure adherence to Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) compliance requirements pertaining to healthcare data protection. Programs for employee awareness and training are also crucial for reducing the likelihood of human mistakes. Healthcare businesses can safely use cloud technology while protecting patient data integrity and privacy by putting in place multi-layered security safeguards. The proposed system provides the multilayer security on healthcare data in cloud environment than other existing systems.

Quantum-safe blockchain-assisted data encryption protocol for internet of things networks

Quantum-safe blockchain-assisted data encryption protocol for internet of things networks

Enhanced Sink Node Method for Protecting Data of Single Owner

Cloud computing services offer computing, storage, networking, and software. Users can access data from any place and any time, under the control of a cloud service provider, by using cloud storage services. CSPs face challenges in protecting their data in the cloud. Public key cryptography can provide security, confidentiality, and integrity. The Schmidt-Samoa cryptosystem (SSC) is a public key cryptosystem that is based on integer factorization hardness. When applying ENHANCED SINK NODE in the cloud, the cryptosystem takes more time to encrypt and decrypt data. In this research, we propose an enhanced Schmidt-Samoa (SSC) public key cryptosystem to address this problem. The ENHANCED SINK NODE cryptosystem uses four prime numbers, which greatly increases the difficulty of breaking the cryptosystem compared to SSC. Further testing showed that the time needed to perform data encryption and decryption in ENHANCED SINK NODE is shorter, but the time needed to do cryptanalysis in ENHANCED SINK NODE is longer than in SSC. The ENHANCED SINK NODE cryptosystem is very secure and makes data more private.

New Lightweight Cryptosystem for IoT in Smart City Environments

Internet of Things (IoT) devices, user interfaces (UI), software, as well as communication networks are all deployed within Smart Cities topology. The security approach designed for Internet of Things IoT should be able to prevent and detect both internal and external attacks. The problem in IoT network that not every linked node or device has an adequate amount of processing power. This means that data encryption and other related activities will be impossible and means that the security of any kind must be lightweight. A trustworthy security solution that stops illegal access to private data on the network is necessary for maintaining the privacy of information on the Internet of Things. Cryptographic processes need to be quicker and more compact without sacrificing security. The aim of this study is to reduce the execution time and power consumption of encryption processes without compromise the complexity of the encryption algorithm. This research presents a new lightweight cryptographic technique to protect various multimedia and real-time traffics across IoT network, by using two S-box in SubByte of encryption process, without affecting its performance. In this study, different audio samples will be used to test the new algorithm efficiency. Comparing the suggested method to the most advanced standard algorithm, it can reduce the cryptography process's execution time as well as energy consumption while maintaining the required security level. The outcomes demonstrate good performance in terms of power usage and delay. The new technique consumed a roughly 0.2 µJ for encryption process while the typical AES algorithm consumed 0.29 µJ, this mean the new algorithm achieved (33% power savings), while maintaining a good complexity level (security) within the process of encryption according to the results in tables I, II, and the comparison in table III. The novelty of this work can be showed by using dual XOR S-box technique which increased the complexity of SubByte process making it more secure without overload the processing performance, in addition to the reduction in encryption rounds which contribute to enhance the performance without compromise the security. Making it more suited for the Internet of Things (IoT) used in smart city environments.

DLSEA-64: dynamic lightweight symmetric encryption algorithm for secure data transmission in IoT based pervasive sensing applications

ABSTRACT Remote patient monitoring, particularly for the elderly, is gaining popularity due to the Internet of Medical Things (IoMT). However, IoMT devices are highly vulnerable to cyber attacks related to data confidentiality. Hence, lightweight cryptographic algorithms are found to be highly necessary to ensure safe data transfer in such devices. This paper introduces a Dynamic Lightweight Symmetric Encryption Algorithm (DLSEA) in order to challenge issues related to real-time data security and privacy. DLSEA is executed on Arduino Uno (ATmega328P) and Arduino Mega (ATmega2560) microcontrollers by using the Proteus simulation tool that employs a 64-bit temporal secret key and a 64-bit block cipher for encryption of data. Several experiments were conducted using payloads of varying sizes to record execution time, energy consumption, RAM usage, ROM usage and avalanche effect. The experiments demonstrated that for data sizes between 128-512 bytes, DLSEA requires 2.49-3.27 ms on the ATmega2560, which is 47% faster than a similar algorithm known as PRESENT. Additionally, DLSEA is 46% more energy-efficient, with RAM usage decreased by 41.81% and ROM usage decreased by 57.19% compared to PRESENT. Experimental results also show that DLSEA satisfies the plaintext sensitivity test, the key sensitivity test, and the rigorous avalanche criteria test (i.e., ~50%).

Issues of personal data protection when using cloud technologies

It is indicated that in today’s digital world, where the amount of stored and processed data is growing at a frantic pace, issues of personal data protection are becoming increasingly important. With the proliferation of cloud technologies that allow companies and individuals to store and process information on remote servers, a new spectrum of threats to data privacy and security is emerging. The article examines the issue of personal data protection when using cloud technologies, in particular, the need to integrate legal and technological aspects to ensure comprehensive protection. The impact of international regulatory acts, such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Protection Act), on cloud data protection processes is analyzed. Particular attention is paid to problems arising from differences in the legal regimes of different countries, which complicate data management at the international level. The article examines in detail modern technical solutions, such as data encryption and multi­factor authentication, which are key elements in protecting confidential information. These technologies have been found to provide a high level of protection, but also have their vulnerabilities. Based on this, it is proposed to invest in the latest technologies, in particular quantum encryption and artificial intelligence, to improve the effectiveness of data protection. Practical cases of data leaks are separately considered, which emphasize the importance of proper configuration of security systems and regular monitoring. Analysis of the results of empirical research shows the need to adapt business processes to changes in the regulatory environment and introduce new technologies to reduce risks. Based on the received data, recommendations for organizations are formulated, which include reviewing data protection policies, investing in modern technologies and training employees. The conclusions of the article emphasize the need for a comprehensive approach to ensuring the protection of personal data in cloud environments. This involves taking into account both modern technologies and current legal requirements to ensure adequate data protection and minimize possible risks.

The Impact of Machine Learning Algorithms and Big Data on Privacy in Data Collection and Analysis

In the era of rapid technological advancements, machine learning (ML) and big data analytics have become pivotal in harnessing vast amounts of data for insights, efficiency, and innovation across various sectors. However, the widespread collection and analysis of data raise significant privacy concerns, highlighting the delicate balance between leveraging technology for societal benefits and safeguarding individual privacy. This article delves into the complexities of data collection and analysis practices, emphasizing the potential for privacy breaches through methods such as location tracking, browsing habits analysis, and the creation of detailed personal profiles. It discusses the implications of ML algorithms capable of de-anonymizing data, despite measures like data anonymization and encryption aimed at protecting privacy. The article also examines the existing legal frameworks, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), designed to enhance privacy protection, alongside the ethical considerations for developers and companies in using ML and big data. Furthermore, it explores future outlooks, including developments in technologies like federated learning and differential privacy, that promise enhanced privacy protection. The conclusion calls for a concerted effort among policymakers, technologists, and the public to engage in ongoing dialogue and develop solutions that ensure the ethical use of ML and big data while upholding privacy rights.

Coordinative Polyimides‐Ln3+ for Full‐Spectrum Luminescence with Applications in PLEDs and Acid‐Responsive Data Encryption

AbstractControllable coordination and efficient sensitization of rare earth ions in polymer materials are key to achieving high‐performance optoelectronic polymers. By designing aromatic diamines, polyimides are synthesized with 1,10‐phenanthroline benzimidazole groups (PBG) in the polymer backbone. Utilizing the coordination between PBG and rare earth ions (Eu3⁺, Tb3⁺), polyimide‐rare earth complexes are formed and precisely characterized their structures. The unique coordination allows PBG to effectively sensitize Eu3⁺, resulting in efficient, long‐lived red emission. Furthermore, PBG maintains excellent coordination with Eu3⁺ in acidic environments, granting acid‐responsive color changes for data encryption. Importantly, PBG sensitization of Tb3⁺ enabled full‐spectrum emission (CIE coordinates: (0.31, 0.35)) within a single rare earth‐polyimide complex, due to electron transfer among the ions, ligands, and polymer. This leads to the design of a multi‐layer PLED device with an optimal external quantum efficiency of 0.43% for white light emission (CIE coordinates: (0.28, 0.34)). Through comparative theoretical and experimental analysis, the photophysical behavior of these coordinated polyimides is explained and explored their photoluminescent and electroluminescent properties. This research integrates the advantages of rare earth elements and polyimides, creating novel luminescent polymers with diverse optical applications, providing a new strategy for designing luminescent coordination polymers.

Synergistic Commutative Algorithm for Securing Vector Spatial Data via GD‐PBIBD Fingerprint Encoding and Data Encryption

Synergistic Commutative Algorithm for Securing Vector Spatial Data via <scp>GD</scp>‐<scp>PBIBD</scp> Fingerprint Encoding and Data Encryption

A secure blockchain framework for healthcare records management systems

AbstractElectronic health records are one of the essential components of health organizations. In recent years, there have been increased concerns about privacy and reputation regarding the storage and use of patient information. In this regard, the information provided as a part of medical and health insurance, for instance, can be viewed as proof of social insurance and governance. Several problems in the past few decades regarding medical information management have threatened patient information privacy. In intelligent healthcare applications, the privacy of patients' data is one of the main concerns. As a result, blockchain is a severe necessity as it can enhance transparency and security in medical applications. Accordingly, this paper uses the design science method to propose a secure blockchain framework for healthcare records management systems. The proposed framework comprises five components: a blockchain network, smart contracts, privacy key management, data encryption, and integration with healthcare information technology. In the proposed framework, healthcare organizations can manage healthcare information securely and privately. Additionally, a secure storage system for electronic records is proposed to meet these organizations' needs. It provides security and privacy for healthcare organizations, especially when managing healthcare information, and also proposes a secure storage system for electronic records to meet the needs of the organizations.

Secure Blockchain Assisted Attribute-Based Keyword Search for Collaborative E-Healthcare

In the age of technological advancement, collaborative E-healthcare emerges as a transformative system eliminating traditional location and accessibility barriers in healthcare services. Here, Searchable Encryption (SE) plays a key role in enabling healthcare providers to outsource encrypted medical data and search services to third parties like cloud servers, thereby reducing storage and management expenses. This intermediary approach poses challenges of single-point failure, privacy breaches, and potentially untrustworthy results. State-of-the-art public key-based SE methods use a cloud-assisted architecture that doesn’t support reliable and practical searches with fine-grained permissions. Also, such systems require additional support to address potential privacy leakages and ensure data availability at the storage server. To address these concerns, we propose a B lockchain-assisted E fficient and S ecure K eyword S earch (BESKS) scheme to enforce fine-grained keyword search privilege control while achieving practical search complexity. Our scheme employs a ciphertext policy attribute-based keyword search mechanism where keywords are encrypted using expressive access policies to build an inverted index structure. The encrypted indexes are stored on the blockchain while encrypted medical documents are stored on InterPlanetary File System (IPFS) nodes to enhance availability and ensure the reliability and scalability of our approach. Our scheme utilizes blockchain-based smart contract for efficient, secure search operations and ensures financial fairness in fine-grained searches. Search tokens are generated based on user attributes and query keywords to facilitate private searches on-chain. To enhance the search process, our secure index enables exact match for a query keyword in constant time to ensure expensive authorization operations are performed only once. Theoretical analysis suggests that our BESKS is more efficient and secure than state-of-the-art schemes. Prototype implementation results on the Ethereum blockchain network further validate its feasibility for real-world applications, demonstrating the scheme's practical applicability in collaborative E-Healthcare systems.

A FRAMEWORK TO ANALYZE OS SYSTEMS ARTIFACTS FROM LINUX MACHINES

Memory forensics plays a pivotal role in digital investigations, providing crucial insights into the activities and artifacts of an operating system. This paper explores the application of deep learning techniques in the domain of memory forensics within the Linux environment. Linux-based systems are widely used in various contexts, including servers, embedded devices, and desktops, making memory analysis in this ecosystem of paramount importance. Traditional memory forensics techniques have relied on manual analysis, which is often time-consuming and error prone. Deep learning, a subfield of machine learning, has demonstrated remarkable capabilities in pattern recognition and feature extraction tasks. In response, this paper presents a novel framework that automates and improves memory analysis through deep learning. Key components of this framework include data collection, preprocessing, feature extraction, and model selection. We introduce a unique dataset specifically curated for Linux memory forensics, facilitating the development and evaluation of deep learning models. Our experimental results demonstrate the efficacy of using a ResNet-50 model for detecting and classifying malware from memory dumps, achieving a detection rate of 98.75% and an accuracy rate of 89% in classifying malware types. Additionally, we acknowledged the challenges and limitations of applying deep learning in memory forensics, such as model interpretability and data privacy concerns. Future research directions are discussed, including real-time memory analysis integration and techniques for handling encrypted and compressed memory data.

Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study

The era of digitization and IoT devices is marked by the constant storage of massive amounts of data. The growing adoption of smart home environments, which use sensors and devices to monitor and control various aspects of daily life, underscores the need for effective privacy and security measures. HE is a technology that enables computations on encrypted data, preserving confidentiality. As a result, researchers have developed methodologies to protect user information, and HE is one of the technologies that make it possible to perform computations directly on encrypted data and produce results using this encrypted information. Thus, this research study compares the performance of three ML models, XGBoost, Random Forest, and Decision Classifier, on a real-world smart home dataset using both with and without FHE. Practical results demonstrate that the Decision Classifier showed remarkable results, maintaining high accuracy with FHE and even surpassing its plaintext performance, suggesting that encryption can enhance model accuracy under certain conditions. Additionally, Random Forest showed efficiency in terms of execution time and low prediction errors with FHE, making it a strong candidate for encrypted data processing in smart homes. These findings highlight the potential of FHE to set new privacy standards, advancing secure and privacy-preserving technologies in smart environments.

FINALLY: A Multi-Key FHE Scheme Based on NTRU and LWE

Multi-key fully homomorphic encryption (MKFHE), a generalization of fully homomorphic encryption (FHE), enables a computation over encrypted data under multiple keys. The first MKFHE schemes were based on the NTRU primitive, however these early NTRU based FHE schemes were found to be insecure due to the problem of over-stretched parameters. Recently, in the case of standard (non-multi key) FHE a secure version, called FINAL, of NTRU has been found. In this work we extend FINAL to an MKFHE scheme, this allows us to benefit from some of the performance advantages provided by NTRU based primitives. Thus, our scheme provides competitive performance against current state-of-the-art multi-key TFHE, in particular reducing the computational complexity from quadratic to linear in the number of keys.

Ransomware Reloaded: Re-examining Its Trend, Research and Mitigation in the Era of Data Exfiltration

Ransomware has grown to be a dominant cybersecurity threat by exfiltrating, encrypting, or destroying valuable user data and causing numerous disruptions to victims. The severity of the ransomware endemic has generated research interest from both the academia and the industry. However, many studies held stereotypical assumptions about ransomware, used unverified, outdated, and limited self-collected ransomware samples, and did not consider government strategies, industry guidelines, or cyber intelligence. We observed that ransomware no longer exists simply as an executable file or limits to encrypting files (data loss); data exfiltration (data breach) is the new norm, espionage is an emerging theme, and the industry is shifting focus from technical advancements to cyber governance and resilience. We created a ransomware innovation adoption curve, critically evaluated 212 academic studies published during 2020 and 2023, and cross-verified them against various government strategies, industry reports, and cyber intelligence on ransomware. We concluded that many studies were becoming irrelevant to the contemporary ransomware reality and called for the redirection of ransomware research to align with the continuous ransomware evolution in the industry. We proposed to address data exfiltration as priority over data encryption, to consider ransomware in a business-practical manner, and recommended research collaboration with the industry.

Carbon Dot-Based Composite with Color Excitation-Dependent Room-Temperature Phosphorescence for Advanced Optical Encryption and Anticounterfeiting.

The phenomenon of multicolor afterglow emission has attracted considerable attention in information encryption, bioimaging, and sensing. Consequently, there is a growing demand for the development of multicolor afterglow and phosphorescence switching methods utilizing carbon dot (CD) materials. Herein, multicolor room-temperature phosphorescence (RTP) emission in CD-based materials (PM-CD@BA composite) was achieved by developing multiple emission centers and tuning the excitation wavelength. The color of the afterglow observed in this composite covered from the deep-blue to the green region. The experimental results reveal that under heating treatment, the CDs embedded in inorganic boric acid/B2O3 matrix materials and a rigid framework generated in the composite system effectively suppressed the nonradiative transition and promoted the RTP emission. Finally, high-resolution multilevel RTP 2D code data encryption was realized by inkjet printing technology. The developed concepts of information encryption and anticounterfeiting exhibit the significant potential of CD-based afterglow materials applied in advanced optical applications.

Role of Encryption/decryption Key in Cryptography

Abstract — Cryptography is a powerful technique for securing data and communication. cryptography is a technique used to encrypt and decrypt the data. A technique encryption (hiding information) data will be encoded which is used to provide secure data to prevent from fraud user and hackers where decryption is used to convert the encoded data into user readable format. It is used for storage and transmission compression is a reduction in the number of bits needed to represent the data."Cryptography algorithms serve the crucial purpose of safeguarding sensitive data. As the internet has seamlessly integrated into our daily lives, experiencing rapid growth over recent decades, ensuring data security has emerged as a paramount concern for every individual connected to the web. Data security guarantees that our information remains accessible solely to its intended recipient while thwarting any unauthorized alterations or tampering attempts. Achieving this high level of security necessitates the utilization of diverse algorithms and techniques. Cryptography encompasses methods that encode data through specific algorithms, rendering it indecipherable to the naked eye unless decoded using predetermined algorithms set by the sender.

The Security of SSH Protocol Public Key Sharing in the Post-Quantum Era

The security of traditional cryptographic schemes is based on mathematical puzzles that cannot be cracked by current computers. However, the rapid development of quantum computers has the potential to greatly reduce the time and resources required to crack these encryption schemes. Although true quantum supremacy may still be years away, it is imperative to adopt anti-quantum algorithms proactively. This preemptive approach aims to thwart "catch first, decrypt later" attacks, wherein attackers intercept and store encrypted data with the intent of decrypting it once quantum computing becomes sufficiently advanced. This threat is particularly critical for protocols like Secure Shell (SSH), which is widely used for secure communication over unsecured networks. In this paper, we propose an innovative approach to enhance the security of public-key distribution within the SSH protocol. Our method integrates quantum-resistant algorithms to ensure that even with the advent of quantum computing, the confidentiality and integrity of SSH sessions are maintained. We emphasize the importance of using cryptographic protocols that operate over open channels, which, while not necessarily confidential, must be authenticated to prevent tampering. In such scenarios, attackers may be able to intercept and even extract information, but they should not be able to alter the data.

Implementation of AES-256 Algorithm for Encryption on Chatting Platforms

This research aims to design and build a web-based corporate chat media using prototype methodology and AES (Advanced et al.) algorithm for data encryption. The platform has been developed with MERN technology (MongoDB et al.), which allows the application to be dynamic and responsive. Prototype methodology is used for iterative development based on user feedback, ensuring the application meets user needs. The AES algorithm is applied to maintain the confidentiality and security of each message sent and received. The results show that the application effectively provides efficient and secure communication for the company, with an intuitive and easy-to-use interface. Implementing MERN technology provides flexibility in the development and maintenance of the application, making it the right solution for corporate communication needs.