Secure Data Access Research Articles

ML-based Intrusion Detection System for Precise APT Cyber-clustering

As more and more documents are converted from hard copies to digital formats and move to cloud storage, securing data access has become a critical and emergent security concern. Without a doubt, intrusion detection system (IDS) has become the primary defense mechanism for governments and enterprises to identify network attacks. However, the emergence of Advanced Persistent Threat (APT) has brought heightened challenges for an IDS, since malicious hackers can deploy various attacks to penetrate information systems invisibly over extended periods of time. Thus, the authors aim to design a High Discrimination APT Intrusion Detection System (HDAPT-IDS); consisting of Cyber Clustering Module (CCM) and Clustering Analysis Module (CAM). CCM conducts a preliminary classification of traffic packets and utilizes the random forest algorithm to predict the main-class, while CAM selects the applicable Deep Neural Network (DNN) based on the prediction results of CCM to derive the sub-class of traffic packets as the final result. Aside from laying out a high detection rate, HDAPT-IDS can effectively reduce the number of categories during classification to achieve better performance.

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.

Beyond the change: exploring age of menopause, lifestyle, and cardiovascular risk in women

Abstract Background Premature/early menopause increases the risk of future cardiovascular disease (CVD); however, whether lifestyle can offset this risk is unknown. Purpose Investigate the association between premature/early menopause and incident CVD and whether a healthy lifestyle modifies this association. Methods The 45 and Up Study prospectively recruited participants aged ≥45 years (n=267,357) between 2005-2009. Our study included women without prior CVD and those reporting menopausal age at baseline. Primary outcome was incident CVD (self-reported heart disease/stroke) based on survey data1 at Wave 2 (2012-2015) and/or Wave 3 (2018-2020). Logistic regression models assessed associations of premature (age <40 years) and early (age 40-44 years) menopause with CVD, compared to standard menopause (age 50-52 years), adjusting for sociodemographic and clinical variables. Healthy lifestyle adherence was assessed using a score of five factors: smoking, physical activity, sitting, sleep, and diet. Interaction effects were analysed between menopause and healthy lifestyle adherence on CVD. Results We included 46,273 women (mean age 62.1±8.2 years), with 5,425(11.7%) incident CVD over 15-year follow-up. After adjustment, the CVD risk was higher in women with premature menopause (odds ratio (OR): 1.36, 95% confidence intervals (CI), 1.17-1.59; p<0.0001) and early menopause (OR: 1.15, 95% CI, 1.03-1.28; p=0.013) compared to standard menopause. Among all menopausal women, high versus low healthy-lifestyle adherence led to 20% lower CVD risk (OR: 0.80, 95% CI, 0.71-0.90; p=0.0003). In the subset of high-risk women with premature menopause, high versus low healthy lifestyle adherence led to 50% lower risk of CVD (OR: 0.50, 95% CI, 0.31-0.80, p=0.0040). The healthy lifestyle effect was not significantly different across menopause groups (p=0.71). Conclusion Early and premature menopause were independently associated with increased CVD risk. Healthy versus unhealthy lifestyle significantly attenuated the risk of CVD in all menopausal women, particularly in the highest risk group of women with premature menopause, reducing this risk by 50%. 1 Secure data access was provided through the Sax Institute’s Secure Unified Research Environment (SURE).

Extreme learning with projection relational algebraic secured data transmission for big cloud data

SummaryCloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA‐SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA‐SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets‐001 dataset in Java. ELPRA‐SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods.

Cybersecurity in a Scalable Smart City Framework Using Blockchain and Federated Learning for Internet of Things (IoT)

Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications.

Securing Automated Intelligence: Challenges and Solutions in RPA and Generative AI Integration

Integrating Robotic Process Automation (RPA) and Generative AI can revolutionize business processes, enabling greater efficiency, scalability, and intelligent decision-making. However, this powerful combination also introduces various security challenges and impacts to governance that organizations must address to protect sensitive data and maintain trust in automation systems. As RPA bots increasingly interact with AI models, vulnerabilities such as unauthorized data access, malicious model manipulation, and improper handling of sensitive information become more pronounced. These risks can lead to cyberattacks, data breaches, and regulatory compliance violations. This paper examines the security challenges inherent in RPA and Generative AI integration, focusing on three key areas: data privacy, model integrity, and automation governance. We assess how improper configurations and lack of security oversight can expose these systems to exploitation. Furthermore, we explore solutions such as implementing robust encryption protocols, secure data access controls, and continuous monitoring of AI model behavior to detect anomalies. By presenting case studies and evaluating emerging best practices, we offer a framework for safeguarding RPA and AI systems, ensuring that automation remains a trusted and secure tool for organizations. The paper also discusses aligning security strategies with regulatory requirements and industry standards. This approach enables organizations to unlock the full potential of RPA and Generative AI while mitigating risks and protecting against evolving cyber threats.

Enhancing Data Warehouse Security: An Encrypted Cued-Click-Points Authentication Approach

Text passwords and other traditional security measures have been the mainstay of current data warehouse content security techniques. Due to the growing computational capability of information systems, security requirements, especially for the selection of secure text passwords, have changed throughout time. Because database administrators are either unaware of these changes or are overburdened by the magnitude of the adjustments necessary to remedy them, these vulnerabilities are typically invisible to them. As a result, research on usable authentication schemes have picked up steam, with the goal of developing a secure authentication method using cued click points and encryption techniques for accessing a hybrid data warehouse. This scheme aims to enhance the security of data access by combining the unique characteristics of cued click points with encryption to create a robust authentication system. The project will involve designing and implementing the authentication scheme, testing its effectiveness in a hybrid data warehouse environment, and evaluating its performance in terms of security and usability. Overall, the project aims to provide a secure and user-friendly authentication solution for accessing sensitive data in a hybrid data warehouse setting. This scheme utilizes a combination of encryption techniques and user-specific click points to verify user identity and access to the data warehouse. It also discusses the potential benefits of this authentication scheme in terms of data security and user experience.

A Novel Method of Secured Data Distribution Using Sharding Zkp and Zero Trust Architecture in Blockchain Multi Cloud Environment

In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates the sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further, we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files (greater than 50 MB) or Big data files (greater than 1 TB) require greater computational complexity, we leverage the sharding concept to enhance both space and time complexity in cloud storage. Hence, the large files are divided into shards and stored in different database servers. We also employ a blockchain smart contract to enhance secure retrieval of the file and also a secure access method, which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data’s authenticity is shared with the verifier at the destination cloud, rather than the actual data themselves. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies required to store data. Since the environment maintains the privacy of the user data and the raw data of the user is not stored anywhere, the entire environment is set up as a Zero trust model.

SAFEGUARDING CHILDREN’S DATA IN THE DIGITAL AGE: A LEGAL APPROACH TO PREVENT CYBERATTACKS AND CHILD SEXUAL ABUSE

The growth of digital technologies and online platforms has dramatically increased the risk of children becoming victims of sexual abuse. Cybercriminals often abuse children's personal information, such as their name, age, location, and online activities, to identifyand exploit them. However, this digital revolution has also led to increased risks, including the vulnerability of children's data to cybersecurity attacks. One of the most concerning consequences of such attacks is facilitating child sexual abuse. Therefore,protecting children's data is essential in preventing child sexual abuse. In this background based on this hypothesis, this paper focuses on the legal approach to protect the vulnerability of children's data from cybersecurity attacks. The paper analysis the legal frameworks and guidelines developed by the Indian government to safeguard the personal data of children. Further, it would identify the current gaps in the legal framework and emphasise the need for further improvements towards strengthening the data protection of children. Moreover, the paper explores the role of technology in protecting children's data, including data encryption, secure data storage, access controls, and monitoring systems and also analyses the challenges associated with implementing these technological solutions. Finally, the paper offers suggestions for policymakers, legal practitioners, and technology professionals to protect children's data from cyberattacks, thereby preventing child sexual abuse.

Multi-factor Authentication Framework for Secured Data Access in Cloud Computing

Aims and Background: Accessing data services on the cloud raises serious concerns about security and privacy. To verify a client's stated identity, the existing authentication approach uses just one authentication factor. However, traditional techniques of one-factor authentication are inefficient since they demand an excessive amount of user information and expose their private data to eavesdropping. Methods: Cloud-based data storage is susceptible to both external and internal threats. Information security relies heavily on authentication, which is a method for verifying user identities before granting them access to protected data. When it comes to protecting data in a cloud computing environment from the most cutting-edge methods of attack, traditional password authentication simply isn't enough. For the purpose of protecting user identities in the cloud, this study presents interactive multi-layer authentication systems. Various access control policies are used when validating users in the cloud. Registration, permissions, and authentication factors for users are all parts of the cloud app's security process. Results: An intrusion detection system is built into the security mechanism to help identify people who could be malevolent. Various techniques are available for user identity verification, including intrusion detection, access control, and multi-factor authentication. The last step is to encrypt the data so that no one else can read it. Conclusion: To guarantee the effectiveness and accuracy of the suggested frameworks and methodology, they are experimentally evaluated. There were few false positives and a high detection rate.

Statistical privacy protection for secure data access control in cloud

Cloud Service Providers (CSPs) allow data owners to migrate their data to resource-rich and powerful cloud servers and provide access to this data by individual users. Some of this data may be highly sensitive and important and CSPs cannot always be trusted to provide secure access. It is also important for end users to protect their identities against malicious authorities and providers, when they access services and data. Attribute-Based Encryption (ABE) is an end-to-end public key encryption mechanism, which provides secure and reliable fine-grained access control over encrypted data using defined policies and constraints. Since, in ABE, users are identified by their attributes and not by their identities, collecting and analyzing attributes may reveal their identities and violate their anonymity. Towards this end, we define a new anonymity model in the context of ABE. We analyze several existing anonymous ABE schemes and identify their vulnerabilities in user authorization and user anonymity protection. Subsequently, we propose a Privacy-Preserving Access Control Scheme (PACS), which supports multi-authority, anonymizes user identity, and is immune against users collusion attacks, authorities collusion attacks and chosen plaintext attacks. We also propose an extension of PACS, called Statistical Privacy-Preserving Access Control Scheme (SPACS), which supports statistical anonymity even if malicious authorities and providers statistically analyze the attributes. Lastly, we show that the efficiency of our scheme is comparable to other existing schemes. Our analysis show that SPACS can successfully protect against Collision Attacks and Chosen Plaintext Attacks.

Retraction notice: Cloud-based secure data storage and access control for internet of medical things using federated learning

Retraction notice: Cloud-based secure data storage and access control for internet of medical things using federated learning

Prevalence and factors associated with mental health status in Parkinson's disease: Data from the 45 and Up Study.

Parkinson's disease (PD) is associated with a range of non-motor symptomologies such as anxiety and depression. The purpose of this research was to investigate the prevalence and factors associated with mental health status in persons with PD. This retrospective data linkage study analysed a subset of data from the Sax Institute's 45 and Up Study based in the population of the state of New South Wales (NSW), Australia. Data was analysed between 2005-2009 of persons who self-identified with PD. Secure data access was provided through the Sax Institute's Secure Unified Research Environment (SURE). Participants were aged 45+ years of age and living in NSW, Australia. Regression analysis was performed on psychological distress (K10), previous diagnosis of anxiety, and/or depression, with independent variables for locality, demographic, health, medical, and sociocultural factors. A total of 1676 persons self-identified with PD from the 45 and Up Study Wave 1 baseline and were linked to the 2010 Social, Economic and Environmental Factors (SEEF) survey dataset. Mental health manifestations are common in PD with 29% and 32% reporting a previous medical diagnosis of anxiety and depression, respectively. Rural locality was associated with an increased likelihood of psychological distress, anxiety, and depression compared to metropolitan locality. Mental health status in PD was associated with living in rural areas, medical service access, female gender, ageing, physical mobility, Australian born, social interactions, and in a coupled relationship. Further research is required on the longitudinal impact of these associations on mental health in PD.

A Robust and Secure Data Access Scheme for Satellite-Assisted Internet of Things With Content Adaptive Addressing

A Robust and Secure Data Access Scheme for Satellite-Assisted Internet of Things With Content Adaptive Addressing

Blockchain and fog computing model for secure data access control mechanisms for distributed data storage and authentication using hybrid encryption algorithm

Blockchain and fog computing model for secure data access control mechanisms for distributed data storage and authentication using hybrid encryption algorithm

Abstract 3568: Managing large-scale cancer research data programs

Abstract Purpose: The National Cancer Institute’s (NCI’s) Cancer Research Data Commons (CRDC) is a data ecosystem for the cancer research community that provides cloud-based, secure storage and analytic tools for many cancer data types, including genomic, proteomic, imaging, and clinical data. CRDC requires a carefully designed, federated data governance framework to enable CRDC’s goals to sustain a collaborative data environment where researchers can access timely, accurate, and relevant cancer data. Methods: In 2023, CRDC chartered a Data Governance Framework of Committees and Working Groups across all CRDC components to: Enable diverse data type sharing; provide secure data access; optimize common infrastructure components and functions; and adhere to FAIR data principles (Findable, Accessible, Interoperable, and Reusable). We considered many designs for the CRDC Data Governance Framework, including fully centralizing or decentralizing decision-making bodies. Implementing a fully centralized data governance framework for a diverse data ecosystem like CRDC would risk top-down decisions that do not meet the unique needs of the CRDC and cancer research community. A fully decentralized data governance framework risks inconsistencies and compliance challenges. Additional governance design considerations included a federated approach that emphasizes clear roles and responsibilities; prioritizing cross-component policies to ensure consistency and promote operational efficiency; defining measurable performance standards; and keeping data owners, stewards, and users informed, trained, and supported. Results: We have determined that long term sustainment of CRDC requires a federated governance approach with broad stakeholder participation through Committees and Working Groups. Currently established CRDC governance committees include the Enterprise Architecture Review Team (EART), Submission Review Team, Data Standards Working Group, and Data Advisory Board consisting of cancer research domain experts, researchers, and skilled cloud engineers and developers. In 2023, the CRDC EART defined data lifecycle stages and identified technical optimization opportunities for submission coordination and indexing. The established governance groups are working to identify optimization opportunities for data quality checks, submission review workflows, study acceptance criteria, data submission reviews, and cross-CRDC emerging data standards requirements. Conclusions: Sustaining large, complex, cancer research data requires a federated governance approach with participation from all relevant stakeholder groups and cannot simply be top-down imposition of policies, standards, and procedures. The newly established CRDC federated governance framework will contribute to improving the long-term sustainability of the CRDC infrastructure and ensuring CRDC data drive meaningful research outcomes. Citation Format: Juergen Klenk, Dina Mikdadi, Chelsea Owens, Angela Maggio, Bhavani Singh, Eric Barner, Tanja Davidsen, Erika Kim. Managing large-scale cancer research data programs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3568.

Cloud-based secure data storage and access control for internet of medical things using federated learning

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

Design of access control framework for big data as a service platform

<span>Big data as a service (BDaaS) platform is widely used by various organizations for handling and processing the high volume of data generated from different internet of things (IoT) devices. Data generated from these IoT devices are kept in the form of big data with the help of cloud computing technology. Researchers are putting efforts into providing a more secure and protected access environment for the data available on the cloud. In order to create a safe, distributed, and decentralised environment in the cloud, blockchain technology has emerged as a useful tool. In this research paper, we have proposed a system that uses blockchain technology as a tool to regulate data access that is provided by BDaaS platforms. We are securing the access policy of data by using a modified form of ciphertext policy-attribute based encryption (CP-ABE) technique with the help of blockchain technology. For secure data access in BDaaS, algorithms have been created using a mix of CP-ABE with blockchain technology. Proposed smart contract algorithms are implemented using Eclipse 7.0 IDE and the cloud environment has been simulated on CloudSim tool. Results of key generation time, encryption time, and decryption time has been calculated and compared with access control mechanism without blockchain technology.</span>

Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

In the realm of secure data access, biometric authentication frameworks are vital. This work proposes a hybrid model, with a 90% confidence interval, that combines "hyperparameter optimization-adaptive neuro-fuzzy inference system (HPO-ANFIS)" parallel and "hyperparameter optimization-convolutional neural network (HPO-CNN)" sequential techniques. This approach addresses challenges in feature selection, hyperparameter optimization (HPO), and classification in dual multimodal biometric authentication. HPO-ANFIS optimizes feature selection, enhancing discriminative abilities, resulting in improved accuracy and reduced false acceptance and rejection rates in the parallel modal architecture. Meanwhile, HPO-CNN focuses on optimizing network designs and parameters in the sequential modal architecture. The hybrid model's 90% confidence interval ensures accurate and statistically significant performance evaluation, enhancing overall system accuracy, precision, recall, F1 score, and specificity. Through rigorous analysis and comparison, the hybrid model surpasses existing approaches across critical criteria, providing an advanced solution for secure and accurate biometric authentication.

Survey of Secure Data Access Control Models in Cloud computing for EHR

Today cloud computing is the first choice of many individuals and different organizations. Computing services offered by different CSP’s are available as per need and the requirement. Most of the healthcare organizations are using cloud services for transmitting, storing and processing electronic health records (EHR) of patients. Patient’s health record is sensitive and there is possibility of violation of privacy by CSP’s. Data access control is one of the best mechanisms to address security issues in cloud computing and ensure the safety, reliability of the system and privacy of the user data. Existing surveys on access control mechanisms in cloud computing focus on all traditional centralized models. However, decentralized access models like block chain with traditional access control model is also used now days for ensuring privacy of EHR. This paper takes the detailed of reviews such existing access control mechanisms in cloud computing, based both types of access control models. Also provides comparisons on each model’s advantages and limitations, and discusses the challenges of, and future research direction for access control.