Secure Data Storage Research Articles

Malicious node detection using SVM and secured data storage using blockchain in WSN

Malicious node detection using SVM and secured data storage using blockchain in WSN

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.

Using Apple Watches to Monitor Health and Behaviors of Individuals with Cognitive Impairment: A Case Series Study.

This study explores the potential of developing digital biomarkers from wearables for monitoring individuals with Alzheimer's Disease and Related Dementias, focusing on the feasibility of using Apple Watches for tracking health and behaviors in older adults with cognitive impairment. Data collection used the Amissa Health technology stack, which passively collects time-series data from smartwatches and provides a high-frequency cloud database for secure data storage, query, and visualization by clinicians and researchers. The platform consists of i) AmissaWear, a software app that runs on smartwatches and sends information to a cloud database using a secure API; and ii) AmissaOrbis, a centralized cloud portal for the collected data. Each participant was provided an Apple Watch configured to collect steps, calories burned, accelerometer and gyroscope readings, heart rate, and sleep information. Seven participants, with cognitive impairment diagnosed by a neurologist, were enrolled in the study from December 2023 through June 2024. The watches successfully collected more than 700,000 observations during the study. Each observation contains data recorded from over a dozen sensors (e.g., heart rate, pedometer, gyroscope, accelerometer). The participants wore Apple Watches for an average of 11.48 hours/day for 84.91% of days during a 6-month period without a decrease in usage over time. Overall, the technology yielded high wear adherence and participation within this pilot. This study demonstrates the feasibility of using widely available Apple Watches for continuous monitoring of individuals with cognitive impairment and provides insights into their daily health and activity patterns, which could aid in future development of digital biomarkers.

An electronic patient-reported outcome symptom monitor: the Chinese experience with rapid development of a ready-to-go symptom monitor

BackgroundMonitoring symptoms is crucial for the early detection of disease progression and timely intervention, which is essential for reducing severe cases and mortality rates in rapidly spreading pandemics, such as COVID-19. Therefore, during infectious disease pandemics, the rapid development of real-time symptom monitoring platforms is essential. This study aimed to explore the urgent development process of an electronic system for patient-reported outcome monitoring in emergency situations.MethodsThe development of the electronic patient-reported outcome COVID-19 symptom monitoring platform (ePRO-CoV-SM) included the following steps: (1) modifying an electronic patient-reported outcome symptom-reporting platform to assess patients with COVID-19 and validating its feasibility and sensitivity for longitudinal symptom measurement; (2) updating the system to accommodate the newly emerged severe acute respiratory syndrome coronavirus 2 BA.2.2 variant; and (3) applying it in real-world settings. Literature review, expert consultation, and subject-group discussions were used to develop symptom items. Response rate and missing item rate were used as validation indicators for ePRO-CoV-SM.ResultsThe ePRO-CoV-SM (2.0) consists of a core set of symptom items, a WeChat mini program, an online project design backend, a management and communication front, and a database. During the 2020 verification, the response rate of ePRO symptom monitoring reached 89.47% and the item missing rate was 0.33%, the monitoring revealed that a considerable number of asymptomatic patients were experiencing undesirable symptoms during the isolation period. In its real-world application in 2022, the response rate was 85.93% and the item missing rate was 4.84%, the monitoring found the symptom burden was higher in the younger group (18–40 years old) than in the older group (40–67 years old), and over 30% of patients reported symptoms such as cough (36.08%), dry mouth (35.67%), sleep disorders (32.27%), appetite loss (32.17%), and sputum (30.79%) during the isolation period.ConclusionsElectronic patient-reported outcome measurement was demonstrated to be sensitive and feasible for monitoring symptoms in patients with COVID-19. By integrating smartphone-based data collection with real-time online data transmission and secure data storage using Secure Sockets Layer encryption, an electronic platform for monitoring critical symptoms can be rapidly established in emergency situations.

Developing a trustworthy national health data space: public needs and policy implications

Abstract Background The ongoing development of a national health data space in Switzerland presents an opportunity to transform health delivery and care by enabling large-scale secondary health research. Central to this development is the Health2030 strategy, which calls for the establishment of public trust in the reliability and security of data collection, storage, and exchange. Methods We conducted four focus groups across the German, French and Italian-speaking regions of Switzerland to inform the development of the Swiss health data space that fosters public trust. To inform Swiss policy, we held a panel discussion with patient experts and healthcare professionals to translate the focus group findings into governance and public communication requirements. We synthesized our findings using a thematic analysis approach and proposed a guiding health policy strategy. Results Citizens perceive the health data space as trustworthy conditional on the delivery of its promised higher levels of transparency, security, and lower risk compared to other data sharing initiatives. To ensure its trustworthiness, citizens call for the provision of value-based consent to control data access based on the actors involved and research purposes, alongside implementing accountability measures for data misuse. Governance framework requirements entail establishing an advisory group for the health data space with sufficient representation of the Swiss population, including members of the public, patient advisory groups, and researchers. Public communication requirements call for ongoing monitoring of public opinion on health data space, alongside establishing public engagement initiatives, such as public fora, to inform ongoing progress. Conclusions Policy efforts should prioritize the meaningful involvement of relevant stakeholders throughout all stages of the health data space development to foster trust. Central to these efforts is balancing public expectations with their practical implementation. Key messages • For the health data space to be trustworthy, citizens require transparency on their data use and the security measures taken, value-based consent for data access, and accountability measures. • Diverse representation in governance and continuous public involvement are key policy measures to build public trust in the development of national health data spaces.

What can we learn from pandemic educational methods?: military general practice trainees’ attitudes to feedback from recorded consultations

ABSTRACT Background Recorded consultations are a useful tool for developing consultation skills for general practice speciality trainees (GPSTs). Historical barriers to utility include a lack of recording equipment and trainee discomfort. Widespread use of online communication platforms during the pandemic led to the introduction of the Recorded Consultation Assessment (RCA), prompting an exploration of its impact on GPSTs’ attitudes and acceptability of using recorded consultations for feedback. Aim This sequential explanatory mixed methods study explored attitudes of military GPSTs towards using recorded consultations for feedback to develop consultation skills, and identify factors influencing GPST attitudes. Methods Participants of this study completed a questionnaire, followed by a representative sample focus group. Descriptive statistics were used to analyse quantitative data, reflexive thematic analysis was employed for qualitative data. Triangulation was conducted using a meta-matrix. Results Results indicated agreement among respondents on the usefulness of recorded consultations for developing consultation skills, particularly communication skills. Perceived trainer attitudes significantly influence the GPST utility of this tool. The RCA positively impacted attitudes, providing familiarity, free access to easy-to-use online recording platforms, simplified consenting procedures, secure data storage, and improved feedback quality from trainers. Conclusion Pre-pandemic studies cited equipment access and consent procedures as barriers to utilising recording as a method of feedback. The pandemic and RCA introduced online resources and imperative to utilise this method, resulting in largely positive GPST learning experiences. As we move away from the RCA it is important to retain institutional memory of the benefits gained from feedback using recorded methods.

Non-volatile and Secure Optical Storage Medium with Multilevel Information Encryption.

Non-volatile photomemory based on photomodulated luminescent materials offers unique advantages over voltage-driven memory, including low residual crosstalk and high storage speed. However, conventional materials have thus far been volatile and insecure for data storage because of low trap depth and single-level storage channels. Therefore, the development of a novel non-volatile multilevel storage medium for data encryption remains a challenge. Herein, a robust, non-volatile, multilevel optical storage medium is reported, based on a photomodulated Ba3MgSi2O8:Eu3+, which combined the merits of light-induced valence (Eu3+ → Eu2+) and photochromic phenomena using optical stimulation effects, accompanied by larger luminescent and color contrasts (>90%). These two unique features provided dual-level storage channels in a single host, significantly improving the data storage security. Notably, dual-level optical signals could be written and erased simultaneously by alternating 265 and 365 nm light stimuli. Theoretical calculations indicated that robust color centers induced by intrinsic interstitial Mg and vacancy defects with suitable trap depths enable excellent reversibility and long-term storage capability. By relying on different luminescent readout mechanisms, the encrypted dual-level information can be accurately decrypted by separately probing the Eu2+ and Eu3+ signals, thus ensuring information security. This study proposes a novel approach for constructing multilevel information storage channels for information security.

Blockchain‐IoT: A revolutionary model for secure data storage and fine‐grained access control in internet of things

AbstractWith the rapid expansion of the Internet of Things (IoT), cloud storage has emerged as one of the cornerstones of data management, facilitating ubiquitous access and seamless sharing of information. However, with the involvement of a third party, traditional cloud‐based storage systems are plagued by security and availability concerns, stemming from centralized control and management architectures. A novel blockchain‐IoT model that leverages blockchain technology and decentralized storage mechanisms to address these challenges is presented. The model combines the Ethereum blockchain, interplanetary file system, and attribute‐based encryption to ensure secure and resilient storage and sharing of IoT data. Through an in‐depth exploration of the system architecture and underlying mechanisms, it is demonstrated how the framework decouples storage functionality from resource‐constrained IoT devices, mitigating security risks associated with on‐device storage. In addition, data owners and users can easily exchange data with one another through the use of Ethereum smart contracts, fostering a collaborative environment and providing incentives for data sharing. Moreover, an incentive mechanism powered by the FileCoin cryptocurrency is introduced, which motivates and ensures data sharing transparency and integrity between stakeholders. Furthermore, in the proposed blockchain‐IoT model, the proof‐of‐authority system consensus algorithm has been replaced by a delegated proof‐of‐capacity system, which reduces transaction costs and energy consumption. Using the Rinkby Ethereum official testing network, the proposed model has been demonstrated to be feasible and economical, emphasizing its potential to redefine IoT data management.

Accelerating Linear Congruential Generators with Carbon Nanotube Field-Effect Transistors

Abstract In the era of technological advancements, safeguarding information privacy has become paramount across sectors like defense, finance, healthcare, and more. At the heart of these security measures lies cryptography, a field that necessitates the efficient processing and secure storage of data. Our project focuses on the optimization of Linear Congruential Generators (LCG), pivotal for cryptographic applications and pseudorandom number generation. Here it has been pioneered the implementation of LCG using Carbon Nanotube Field-Effect Transistor (CNTFET) technology, targeting the reduction of leakage power through the innovative application of sleep techniques. This advanced approach not only embodies a leap towards minimizing energy consumption but also enhances the overall performance and reliability of cryptographic systems. By integrating sleep modes into the CNTFET-based LCG design, our methodology significantly curtails leakage power without compromising the generator’s efficiency or output quality. The successful implementation and synthesis of this design, employing cutting-edge CNTFET technology, underscore the potential for substantial improvements in power efficiency while maintaining high standards of security and randomness essential for cryptographic algorithms.

From Sensors to Standardized Financial Reports: A Proposed Automated Accounting System Integrating IoT, Blockchain, and XBRL

Modern advances in technology have increased the demand for traditional accounting systems to be upgraded for real-time data processing, security, and standardized reports. Thus, this paper proposes a new accounting information system that integrates IoT, blockchain, and XBRL. The proposed system aims to automate the accounting process by using IoT to collect data and send it automatically to a blockchain, which acts as a database that will generate journal entries automatically through smart contracts. XBRL will then be used as an output method for standardized financial reports based on the data transferred from the blockchain. This paper uses a qualitative research design based on semi-structured interviews with 13 industry experts from IT engineering, academia, and financial systems analysis. NVivo software was used to conduct a thematic analysis of interview transcripts. The findings demonstrated that integrating IoT, blockchain, and XBRL is technically feasible, with significant potential to enhance accounting systems. Additionally, the findings identified key challenges of the proposed system, including the complexity of integration, data validation across technologies, costs, user adoption, and scalability concerns. However, the results showed that this system offers substantial benefits, such as real-time data capture from IoT devices, secure data storage and immutability through blockchain, standardized financial reporting via XBRL, accounting process automation, improved data accuracy, and enhanced security and transparency in financial reporting. The study also identified an optimal mechanism for ensuring seamless data transmission between these technologies. The study makes a valuable contribution to the accounting field by providing a new framework for automating data collection, enhancing data security, and streamlining financial reporting, with significant potential to advance accounting systems and improve transparency, accuracy, and efficiency in financial reporting. The study’s potential to impact accounting systems and financial reporting research and practice emphasizes its importance.

A cross-chain-based approach for secure data sharing and interoperability in electronic health records using blockchain technology

In most healthcare facilities, Electronic Health Records (EHRs) have changed paper-based medical records in the healthcare industry. Nevertheless, there are issues with credibility, management, and secure data storage with the existing EHR frameworks. In order to fill this, this work establishes a cross-chain approach for EHR record sharing that is based on the interoperability of EHRs. Different entities, like patients, healthcare providers, Government Agencies (GA), blockchain, and ledger are included in the proposed system. Several processes including initialization, registration process, agreement and verification process, bank fund transfer and certificate generation, health record exchange among two hospitals, and secret key generation, are used to implement the newly developed authentication approach. In addition, the key is obtained from the interior layers of Deep Residual SpinalNet (Deep RSNet), which is utilized to encrypt the EHR records. The Deep RSNet is the integration of Deep Residual Network (DRN) and SpinalNet. The investigation of the developed technique exposes the supremacy of the Deep RSNet model by achieving minimal values of response time, memory usage, and normalized variance of 53.677 sec, 2.682 MB, and 1.346 respectively.

A hybrid architecture for secure data sharing in multi-clouds system

Abstract Cloud computing is one of the most cutting-edge technologies around the world that plays a major role in IT industries and personal use. Several sectors are showing efforts in adopting cloud computing to their services, considering the provided cost reduction and process efficiency. However, outsourcing users’ sensitive data increases the concerns regarding the security, privacy and integrity of stored data. Therefore, there is a need to build a trusting relationship between users and cloud systems. Hence, in this paper, we propose a privacy-preserving framework, called Hybrid and Secure Data Sharing Architecture (HSDSA), for secure data storage in cloud systems. The basic idea of HSDSA is to improve data security in a multi-cloud environment using a combination of cryptography techniques. These techniques ensure that the user has total control over the data generation and management of the decryption without relying on a trusted authority. HSDSA provides removal of centralized file storage distribution and ensures data integrity in the recovery process. We evaluate our contribution under Cloudera, and the results demonstrate the efficiency of HSDSA compared with the existing system.

A review of research on secret sharing

This article explores the importance and challenges of secret sharing technology in the context of rapid advancements in information technology. With increasing attention on data security and privacy protection, secret sharing serves as a key cryptographic technique that ensures secure data transmission, storage, and multi-party computation by dividing secrets into multiple shares. However, existing solutions encounter performance bottlenecks when dealing with large-scale datasets, managing share updates in dynamic environments, and facing quantum computing threats. This article systematically summarizes the basic concepts, theories, classifications, and characteristics of secret sharing while analyzing its applications in financial security, medical privacy, and other fields. Furthermore, it presents recent research progress on dynamic secret sharing and anti-quantum secret sharing. The findings indicate that dynamic secret sharing schemes improve long-term key storage security, while anti-quantum secret sharing provides a solution to counteract quantum computing threats. This study not only deepens understanding of secret sharing technology but also offers insights for future research directions, significantly contributing to enhancing data protection levels.

Obfuscating Ciphertext-Policy Attribute-Based Re-Encryption for Sensor Networks with Cloud Storage

With the rapid growth of wireless sensor networks, secure data transmission, storage, and distribution in such networks has become an urgent demand. To defend against security risks such as data leakage, key compromise, and unauthorized misuse of data simultaneously, we propose a novel obfuscatable ciphertext-policy attribute-based re-encryption scheme with a specially designed obfuscator. The proposed scheme leverages program obfuscation to transform the re-encryption program codes into an unintelligible form and embed the private keys into the obfuscated implementation. Consequently, the proposed scheme protects data confidentiality and keeps the secrecy of the private key while providing fine-grained access control. Formal proofs for the security of the proposed re-encryption scheme and the obfuscator are provided. Extensive experiments have been conducted on representative platforms, including cloud servers, workstations, and embedded devices, to evaluate the computational efficiency and energy consumption of the scheme. Experimental results indicate that the scheme achieves high efficiency on various platforms and economical energy consumption on typical embedded devices with constrained resources.

Disjoint paths construction algorithm in the data center network DPCell

Abstract With the development of the fourth industrial revolution, the importance of data centers has significantly increased. Data centers are widely used in many fields due to their ability to provide efficient, secure, and reliable data storage and processing services. However, with the increasing amount of data, traditional data center networks (DCNs) are currently facing various challenges, prompting academia and industry to propose new DCN architectures. As a dual-port server-based DCN, DPCell has excellent scalability and bisection width, enabling it to meet the demands of large-scale data storage, processing, and computation in the digital revolution. In order to ensure the secure and reliable data communication in the DPCell, this paper designs a disjoint paths communication scheme based on the actual DCN routing requirements. This scheme constructs the optimal number of disjoint paths in DPCell, with a maximum path length of $2^{k}+3$, where $k$ represents the dimension of the DPCell. Furthermore, experiments have verified that the time complexity of this scheme is sublinear, making it more efficient than the current optimal maximum flow algorithm. To a certain extent, this scheme provides DPCell with the required high bandwidth, fault tolerance, and security for data communication.

Applications of blockchain technology in peer-to-peer energy markets and green hydrogen supply chains: a topical review

Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.

FBLearn: Decentralized Platform for Federated Learning on Blockchain

In recent years, rapid technological advancements have propelled blockchain and artificial intelligence (AI) into prominent roles within the digital industry, each having unique applications. Blockchain, recognized for its secure and transparent data storage, and AI, a powerful tool for data analysis and decision making, exhibit common features that render them complementary. At the same time, machine learning has become a robust and influential technology, adopted by many companies to address non-trivial technical problems. This adoption is fueled by the vast amounts of data generated and utilized in daily operations. An intriguing intersection of blockchain and AI occurs in the realm of federated learning, a distributed approach allowing multiple parties to collaboratively train a shared model without centralizing data. This paper presents a decentralized platform FBLearn for the implementation of federated learning in blockchain, which enables us to harness the benefits of federated learning without the necessity of exchanging sensitive customer or product data, thereby fostering trustless collaboration. As the decentralized blockchain network is introduced in the distributed model training to replace the centralized server, global model aggregation approaches have to be utilized. This paper investigates several techniques for model aggregation based on the local model average and ensemble using either local or globally distributed validation data for model evaluation. The suggested aggregation approaches are experimentally evaluated based on two use cases of the FBLearn platform: credit risk scoring using a random forest classifier and credit card fraud detection using a logistic regression. The experimental results confirm that the suggested adaptive weight calculation and ensemble techniques based on the quality of local training data enhance the robustness of the global model. The performance evaluation metrics and ROC curves prove that the aggregation strategies successfully isolate the influence of the low-quality models on the final model. The proposed system’s ability to outperform models created with separate datasets underscores its potential to enhance collaborative efforts and to improve the accuracy of the final global model compared to each of the local models. Integrating blockchain and federated learning presents a forward-looking approach to data collaboration while addressing privacy concerns.

Automatic Intrusion Detection Model with Secure Data Storage on Cloud Using Adaptive Cyclic Shift Transposition with Enhanced ANFIS Classifier

Cloud computing has emerged as a pivotal technology in the computer electronics industry, offering users significant computing power and ample storage space. Security threats pose significant challenges to the progression of cloud computing, hindering its growth in the industry. Detecting intrusions is crucial for protecting cloud environments from harmful attacks. However, due to the complexity and vast amount of network data, building effective intrusion detection systems (IDS) for cloud setups is difficult. Traditional IDS have struggled to effectively mitigate these risks. To overcome these problems, we propose a novel feature selection technique with deep learning classifier-based intrusion detection and avoidance in a cloud environment. The suggested model is divided into four phases: feature selection, pre-processing, classification, and encryption. The initial step involves gathering the data from the dataset and pre-processing it. The Adaptive Walrus Optimization Algorithm (AWO) is then used to choose select optimal features, aiming to mitigate computational complexity and reduce time consumption. These selected features are then fed into an enhanced Adaptive Neuro-Fuzzy Inference System (EANFIS) classifier for accurate classification of normal and intruded data. Following classification, normal data undergoes encryption using the Adaptive Cyclic Shift Transposition (ACST) Algorithm to bolster security.For experimental evaluation two datasets used namely, KDDCup-99 and NSL-KDD. The proposed method notably achieves impressive accuracy rates of 98.47% for the NSL KDD dataset and 98.97% for the KDD-CUP99 dataset.

QRGB+: Advanced QR Code Generator with RGB Color Method in Python to Expand Data Capacity

The present work entitled QRGB, consists of the development of an application in Python for the generation of QR codes using the additive color generation (RGB) method. This innovative method allows increasing the density of information stored in QR codes by using three color layers (red, green and blue), each representing a different set of data. QRGB offers an efficient and secure solution for storing and transmitting large amounts of information in limited spaces, significantly improving the capabilities of traditional black and white QR codes. By using three layers of colors, QRGB codes can store up to three times more information in the same space. This technique not only increases storage capacity but also improves information security, making it difficult to forge or manipulate the code. The overlay of multiple data layers allows redundancy to be implemented, increasing the robustness of the code against damage or reading errors. QRGBs are especially useful in applications that require the transmission of large amounts of data in limited spaces, such as in the packaging industry, digital business cards, and interactive advertising. Additionally, they have great potential in areas such as document and banknote security, where the authenticity and integrity of information are crucial. These points provide a solid foundation for understanding the innovation and advantages of colored QR codes (QRGB) compared to traditional QR codes, highlighting their applicability and potential in various sectors. This article presents a novel method for encoding and decoding information using a QRGB code, which involves the generation of three independent QR codes and their superimposition according to the additive color system (RGB). The research highlights the challenges encountered during the encoding and decoding processes due to the lack of specific libraries in Python, which required the creation of a custom solution using open source tools. The implementation takes advantage of Python and its libraries: qrcode[pil] to generate QR codes with the Pillow dependency for image manipulation, Pillow to open, manipulate and save different image formats, and opencv-python to perform tasks such as image processing and object detection. Despite facing issues with color mixing and accurate information retrieval, the proposed method demonstrates a significant increase in data density within a single QR code. Future work will focus on optimizing the algorithm and exploring potential applications in data security and high-density information storage. This Python script is designed to generate and decode QR codes with a logo overlay using a graphical user interface (GUI) built with Tkinter. The script combines several functionalities, such as creating QR codes, overlaying a logo, combining QR images of different colors, and manually decoding combined QR codes.

Mixed fragmentation technique for securing structured data using multi-cloud environment (MFT-SSD)

Large data storage security is a topic of great interest to researchers, particularly in the age of big data where preserving data from theft, unauthorized access, and storage failure has become a crucial concern. To safeguard such data, encryption/decryption approaches have been employed, which are time-consuming and inefficient. The aim of this study is to develop a method, namely Mixed Fragmentation Technique for Securing Structured Data using Multi-Cloud Environment (MFT-SSD), for protecting large-scale data stored in a multi-cloud environment. This prevents insider attacks by adopting a mixed fragmentation approach to split the data into three files. For example, healthcare data is will be distributed among many clouds, each of which stores a partially unrecognized fraction of data without the need for an encryption or decryption layer. Comparing MFT-SSD to various encryption/decryption algorithms, our results show significant improvement; hence, the total performance of big data security is also improved.