scispace - formally typeset
Search or ask a question

Can blockchain-based solutions provide better security for cloud-based applications compared to traditional methods? 


Best insight from top research papers

Blockchain-based solutions offer enhanced security for cloud-based applications compared to traditional methods. Traditional methods face challenges like key management issues, single points of failure, and high trust costs. In contrast, blockchain technology ensures distributed trust, immutability, and transparency, mitigating security vulnerabilities caused by centralization. Blockchain-assisted techniques address key management problems, enable dynamic access permission updates, and provide efficient user revocation. These solutions combine attributes like confidentiality, fine-grained access control, and secure data sharing while preventing data tampering and unauthorized access. By leveraging blockchain for authentication, authorization, encryption, and verification, cloud-based applications can achieve superior security, scalability, and efficiency, making them more resilient to cyber threats and ensuring trustworthy operations in cloud environments.

Answers from top 5 papers

More filters
Papers (5)Insight
Blockchain-based solutions, like the proposed multi-factor access control mechanism, offer enhanced security for cloud-based applications by overcoming single-point-of-failure issues and providing efficient authentication methods.
Blockchain-assisted verifiable scheme enhances security for remote sensing image retrieval in the cloud, ensuring integrity and preventing malicious behavior, surpassing traditional methods' security.
Yes, a Blockchain-based Anonymous Attribute-based Searchable Encryption Scheme enhances security for cloud data sharing by providing confidentiality, tamper-proof features, integrity verification, and non-repudiation, reducing single points of failure.
Yes, blockchain-based solutions offer enhanced security for cloud-based applications by enabling distributed trust, dynamic access control updates, and efficient storage, as shown in the proposed scheme.
Blockchain-assisted key management in CP-ABE enhances security for cloud-stored data by addressing key issues like key escrow, distribution, and update, offering improved security compared to traditional methods.

Related Questions

How does scalability challenges in blockchain enabled cloud security ?5 answersScalability challenges in blockchain-enabled cloud security arise due to the limitations of traditional centralized models,. Blockchain's inherent data structure and consensus mechanisms pose scalability issues, especially with the increasing demand in 5G and B5G environments. To address this, innovative approaches like sharding and redactable blockchains have been proposed. Sharding enhances scalability by reducing storage pressure on nodes and increasing transaction processing speed in Industrial Internet of Things (IIoT) environments. On the other hand, redactable blockchains, like in ScalaCert, mitigate scalability problems by directly recording revocation information on certificates, reducing storage overhead. These advancements aim to improve system scalability while ensuring security in blockchain-enabled cloud security applications.
What are the potential privacy-preserving techniques that can be implemented using blockchain in cloud computing?5 answersBlockchain can be used to implement privacy-preserving techniques in cloud computing. One potential technique is the use of a Secure Recommendation and Training Technique (SERTT) that combines federated learning and blockchain approaches to protect the privacy of Internet of Medical (IoMT) recommender system data. Another technique is the Privacy-preserving Identity Management (PPIdM) system, which uses blockchain to manage users' identities and protect their privacy by enabling authentication without disclosing real identities. Additionally, a verifiable Attribute-Based Encryption (ABE) scheme based on blockchain and local differential privacy can be used to protect privacy in virus-infected patient tracing systems. Furthermore, an efficient blockchain-based privacy-preserving scheme with attribute and homomorphic encryption can provide fine-grained secure access control and protect user privacy in distributed intelligent systems. Lastly, blockchain-assisted conditional anonymity privacy-preserving public auditing (BA-CAPPPA) schemes can be used to protect data integrity, whistleblower identity privacy, and accurately trace responsibility in cloud storage systems.
How can blockchain be used to improve the security and efficiency of cloud computing?5 answersBlockchain technology can be used to improve the security and efficiency of cloud computing. By using an immutable, distributed, and decentralized record of transactions, blockchain provides a secure and auditable data protection system. The use of cryptographic hashes ensures the accuracy and authenticity of data stored on the blockchain, making it difficult for data to be tampered with or corrupted. Additionally, blockchain technology offers scalability, efficiency, and cost savings compared to traditional databases, as it requires fewer resources and can process a larger volume of transactions. The decentralized nature of blockchain allows it to work across multiple devices and networks, further enhancing its efficiency and cost-effectiveness. Overall, blockchain technology provides a secure and efficient solution for protecting data in cloud computing environments.
What is blockchain security applications?5 answersBlockchain technology has various security applications in different domains such as IoT, education, e-commerce, and healthcare. It provides a decentralized and tamper-proof data management system that ensures trust and security in transactions. In the education system, blockchain technology guarantees the security and confidentiality of data, increasing trust among stakeholders. In e-commerce, blockchain enhances transaction security and simplifies payment systems, making them more efficient and well-documented. In healthcare, blockchain technology offers advantages in protecting data confidentiality, integrity, and availability, addressing security issues in medical health data storage. Overall, blockchain technology provides a secure and trustworthy platform for various applications, ensuring data protection and enhancing trust in transactions.
How can blockchain technology be used to improve security?5 answersBlockchain technology can be used to improve security in various ways. Firstly, it can enhance cybersecurity by providing transparency, integrity, and resilience to organizations and individuals in mitigating cyber threats. Secondly, it can increase customer security and trust in online transactions in the e-commerce industry. Thirdly, blockchain can be used to secure networks, particularly in the field of Internet of Things (IoT). Fourthly, it can enable secure data transmission and device processing in distributed networks, such as the IoT network. Lastly, blockchain ensures data security by collecting, storing, and disseminating information in a tamper-proof manner, making it difficult for anyone to alter or delete data.
How does blockchain provide security in cloud?5 answersBlockchain provides security in cloud by utilizing encryption, data verification, and decentralized record-keeping. It ensures data integrity and reduces vulnerability to tampering or corruption. By encrypting data and storing it on the blockchain, clients can quickly determine if their data has been tampered with before the cloud service provider becomes aware of any potential security breaches. Blockchain technology uses cryptographic hashes to verify the accuracy and authenticity of data stored on the blockchain, making it difficult for any changes or modifications to go undetected. Additionally, blockchain provides a permanent and immutable record of transactions, further enhancing data security. The decentralized nature of blockchain allows it to work across multiple devices and networks, making it a more efficient and cost-effective solution for securing data in the cloud.

See what other people are reading

How to mitigate AI hallucinations?
5 answers
To mitigate AI hallucinations, various approaches have been proposed. One method involves actively detecting and mitigating hallucinations during the generation process. Another approach focuses on evaluating the percentage of the source contribution to a generated translation to identify and alleviate hallucinations, either using internal model characteristics or external tools like cross-lingual embeddings. Additionally, the use of Natural Language Inference (NLI) entailment metrics has been explored to detect and prevent hallucinations in summary generation tasks, showing significant improvements over conventional methods like beam search or fact checkers. These strategies aim to enhance the reliability and trustworthiness of large language models by effectively reducing the occurrence of hallucinations in generated text.
Issue fnb business in supply chain managament
4 answers
The issue of financing in supply chain management is a critical aspect addressed in the literature. Various financing practices, such as trade credits, guarantor practices, and joint financing, are explored to support capital-constrained firms. Additionally, the importance of technology in enabling sustainable supply chain management in base of the pyramid (BoP) markets is highlighted, emphasizing the role of technological advancements in modernizing supply chains and addressing unique challenges faced in informal markets. Furthermore, the presence of collaborative business processes within the supply chain, particularly in the fashion industry, underscores the significance of successful business process management (BPM) for enhancing process maturity and improving supply chain operations. These insights collectively shed light on the multifaceted issues and solutions in supply chain management, ranging from financing strategies to technological innovations and collaborative process management.
Is there chatbot research regarding to psi and as personality as moderator?
4 answers
Research on chatbots has explored the impact of personality matching between chatbots and users. Personality-driven chatbots have been investigated in various contexts, such as facilitating deliberative discussions and consensus-reaching, enhancing user experiences in enterprise chatbots across different domains, and improving user engagement in mental health care settings. Additionally, chatbots with specific personalities have been designed through co-design approaches involving migrants and stakeholders, highlighting the role of chatbot personality in driving the co-design process. While these studies focus on different aspects of chatbot interactions, they collectively emphasize the significance of personality in shaping user experiences, engagement, and the effectiveness of chatbots in various domains.
How effective were the projects that are made already for the lung cancer detection and prediction ?
5 answers
The projects developed for lung cancer detection and prediction have shown promising results. Various methods have been employed, such as computer-aided diagnostic (CAD) systems utilizing convolutional neural networks (CNNs), deep neural networks trained on histopathological lung cancer tissue images, and machine learning techniques for accurate predictions. These approaches have significantly improved the accuracy, precision, recall, and specificity in detecting lung cancer cells, achieving high values such as 97.09% accuracy, 96.89% precision, 97.31% recall, 97.09% F-score, and 96.88% specificity. The utilization of advanced technologies like deep learning, image processing, and ensemble classifiers has enhanced the efficiency and reliability of lung cancer diagnosis, offering a more effective means of early detection and treatment initiation.
What inforamtion can Hyperspectral VISNIr add to sentiel-2?
5 answers
Hyperspectral VISNIR data can complement Sentinel-2 imagery by providing enhanced spectral resolution for detailed analysis in various applications. Hyperspectral sensors like Hyperion, PRISMA, and HISUI cover wavelengths not available in Sentinel-2, offering additional information for vegetation, agriculture, soil, geology, urban areas, land use, water resources, and disaster monitoring. Additionally, the simulation of hyperspectral data from Sentinel-2 using techniques like the Uniform Pattern Decomposition Method (UPDM) has shown improved classification accuracy for land cover mapping, surpassing the capabilities of Sentinel-2 data alone. Emulators developed through machine learning techniques can generate synthetic hyperspectral images based on the relationship between Sentinel-2 and hyperspectral data, providing highly-resolved spectral information for large areas efficiently.
Does AI track time and financial management?
5 answers
Yes, AI plays a significant role in tracking time and financial management. By integrating artificial intelligence (AI) and data mining, smart financial real-time control systems are developed to enhance financial management efficiency and create economic value for enterprises. Additionally, AI-driven management systems, such as TradingBots, autonomously perform real-time decision-making processes in financial institutions, leading to improved performance and cost-effectiveness. Moreover, AI-based finance manager applications offer users a more engaging experience by providing transaction charts, expense analysis, and automated data entry for recurrent transactions, thereby facilitating better financial tracking and management. In essence, AI not only tracks time and financial activities but also optimizes processes, enhances decision-making, and improves overall financial management practices.
What are the advantages and limitations of using RNA-seq data for allele specific analysis pipelines?
5 answers
RNA-seq data offers advantages and limitations for allele-specific analysis pipelines. Advantages include the ability to detect and quantify alleles expressed under different conditions without the need for DNA sequencing or haplotype knowledge. Additionally, a spike-in approach can reduce costs significantly while maintaining accuracy in allele-specific expression analysis. On the other hand, challenges persist in accurately aligning reads containing genetic variants, which can lead to biases in ASE detection. The Personalised ASE Caller (PAC) tool addresses this by improving the quantification of allelic reads, reducing incorrect biases and increasing the reliability of ASE detection, especially in small sample sizes or when studying rare genetic variations. Furthermore, targeted RNA-seq (tar-RNAseq) proves beneficial in improving SNP coverage and concordance of ASE values, particularly in human studies with limited SNPs and degraded RNA samples.
When Songs Cross Language Borders: Translations, Adaptations and ‘Replacement Texts’?
4 answers
When songs cross language borders, they can undergo translations, adaptations, or be transformed into 'replacement texts' depending on the degree of fidelity to the original source material. Singable translations may deviate from strict semantic fidelity, leading to significant changes that classify them as adaptations rather than translations. Adaptations and continuations of songs come in various forms, including novels, comics, and stage adaptations, raising questions about literary property and the nature of continuation with unstable or orphaned texts. Interlingual cover versions of popular songs, like Tarkan and Sezen Aksu’s 'Simarik', have been circulated globally in multiple languages, showcasing the diversity of approaches in studying this phenomenon and the various factors influencing production and reception of such covers. The concept of adaptation, prevalent in both Western and Far Eastern translation practices, challenges the traditional dichotomy between translation and adaptation, emphasizing the importance and validity of adaptations in the realm of text mediation.
What are the impacts of AI in grade 10 students?
5 answers
The impacts of AI on Grade 10 students are multifaceted. AI applications like the Smart Teacher platform and LearningML enhance teaching and learning functions by providing self-learning opportunities, real-time feedback, and personalized recommendations. Additionally, AI-based systems like AI-PANS aim to improve students' performance by generating tailored question papers, adjusting difficulty levels based on performance, and offering curated solutions to enhance problem-solving skills. However, it is crucial to consider potential negative impacts, as research suggests that AI in education can negatively affect social adaptability in adolescents, particularly through family support. Overall, integrating AI in education can revolutionize learning experiences, making them more effective, personalized, and inclusive, while also necessitating a careful evaluation of its broader implications on students' social and emotional well-being.
How does the amount of data required for deep learning vary depending on the application?
5 answers
The amount of data required for deep learning varies depending on the application. In general, deep learning models demand a large volume of data to achieve high performance. Insufficient data can lead to challenges such as overfitting and reduced generalization capabilities. Different fields like computer vision, natural language processing, security, and healthcare necessitate large datasets for effective training. Moreover, the precision of a trained deep learning model may not generalize well to new test datasets, emphasizing the need for adequate and augmented training data. Active transfer learning-based approaches have been proposed to address data scarcity issues, enabling accurate predictions with reduced data requirements. Therefore, the data requirements for deep learning applications vary widely, with some fields requiring extensive datasets for optimal model performance.
What are the current uses of machine learning in IMU data?
5 answers
Machine learning is extensively utilized in IMU data for various applications. In healthcare, wearable devices leverage Machine Learning algorithms to enhance Human Activity Recognition (HAR). IMU sensors, combined with Machine Learning methods, enable terrain topography classification, sports monitoring for exercise detection and feedback, and deep learning models for feature extraction from unlabeled IMU data, improving Human Activity Recognition tasks. These applications showcase the versatility and effectiveness of Machine Learning in processing IMU data for tasks ranging from activity recognition to terrain classification and sports monitoring.