Blockchain-Driven Security for Scalable Big Data | #sciencefather #database #scientistaward #CloudSecurity #BlockchainSecurity

A Multi-Layered Blockchain Architecture for Energy-Efficient and Privacy-Aware Big Data Security in Cloud Systems 

Blockchain-Based Trust Management

In cloud computing environments, ensuring trust among distributed entities is a major challenge due to the absence of a centralized authority. IBMESR introduces a novel spatial and temporal trust-based blockchain consensus mechanism that evaluates miner nodes based on their location and behavior over time. This adaptive trust model enhances the reliability of block additions while reducing the risk of malicious node participation. By leveraging these dynamic trust scores, IBMESR ensures a more secure and responsive blockchain network for real-time big data transactions.


Dynamic Blockchain Sharding with GWO

As the volume of data increases in cloud systems, blockchain scalability becomes a critical concern. To address this, IBMESR incorporates Grey Wolf Optimization (GWO) to perform dynamic blockchain sharding, optimizing how sidechains are configured based on workload and trust metrics. This intelligent sharding reduces bottlenecks, enhances throughput, and allows parallel processing of data blocks. The use of GWO, inspired by the hunting behavior of grey wolves, provides an efficient, nature-inspired way to balance security with performance in high-traffic environments.

Full Homomorphic Encryption (FHE) for Data Confidentiality

One of the standout features of IBMESR is its use of Full Homomorphic Encryption, which allows computations to be performed directly on encrypted data. Unlike traditional encryption methods that require decryption before processing (introducing vulnerability windows), FHE ensures that sensitive data remains protected throughout its lifecycle. This is particularly useful in multi-tenant cloud architectures where multiple users or systems may access shared infrastructure. FHE helps maintain data privacy and integrity without compromising on performance or usability.

Hardware-Level Security via Physical Unclonable Functions (PuFs)

Security breaches can also stem from the physical layer, including device tampering or spoofing. IBMESR addresses this by integrating Analog Physical Unclonable Functions (PuFs), which provide unique, hardware-based digital fingerprints for cloud nodes. These fingerprints are used to establish secure communication channels and authenticate devices without relying on stored keys that could be stolen. This ensures strong resistance to physical cloning and significantly reduces the attack surface at the hardware level.

Context-Aware Fuzzy Access Control

Traditional access control models like RBAC and ABAC struggle to respond dynamically to evolving threats. IBMESR integrates a fuzzy logic-based access control system that takes into account contextual variables such as user behavior, access location, and time of request. This rule-based system assigns varying degrees of trust to access requests and makes intelligent decisions even in uncertain or borderline cases. As a result, the model can accurately detect and prevent unauthorized access, including sophisticated attacks like spoofing, injection, and cross-site scripting.

Performance Evaluation and Results

The IBMESR framework was rigorously tested under both attack and non-attack scenarios to evaluate its resilience, efficiency, and scalability. Metrics such as mining delay, energy consumption, communication throughput, jitter, and attack detection accuracy were used for assessment. Results showed a marked improvement over conventional models, with lower delays, reduced power usage, and enhanced security metrics. Notably, the system's attack detection accuracy exceeded 95%, confirming its ability to defend against complex cyber threats in real-time environments.

Real-World Applicability and Future Directions

IBMESR is particularly suitable for critical infrastructure applications like healthcare, finance, and IoT systems, where data integrity, availability, and confidentiality are paramount. Its modular architecture allows for easy integration with existing cloud platforms. Future enhancements may include machine learning-driven trust estimation, quantum-resistant cryptography, and federated learning compatibility to further strengthen its capabilities in emerging digital ecosystems.

#CloudSecurity#BlockchainSecurity#CyberSecurity#DataPrivacy#HomomorphicEncryption#AccessControl#TrustedComputing#BlockchainTechnology

International Database Scientist Awards
Contact Us For Enquiry: contact@databasescientist.org

#DatabaseScience #DataManagement #DatabaseExpert #DataProfessional #DatabaseDesign #DataArchitecture #DatabaseDevelopment #DataSpecialist #DatabaseAdministration #DataEngineer #DatabaseProfessional #DataAnalyst #DatabaseArchitect #DataScientist #DatabaseSecurity #DataStorage #DatabaseSolutions #DataManagementSolutions #DatabaseInnovation #DataExpertise

Comments

Post a Comment

Popular posts from this blog

Large Language Models and Vector Databases for News Recommendations

Image
  Large Language Models   (LLMs) generated a global buzz in the machine learning community with recent releases of generative AI tools such as Chat-GPT, Bard, and others alike. One of the core ideas behind these solutions is to compute a numerical representation of unstructured data (such as texts and images) and find similarities between these representations. However, taking all of these concepts into a production environment has its own set of machine learning engineering challenges: How to generate these representations quickly? How to store them in a proper database? How to quickly compute similarities for production environments? In this article, I introduce two open-source solutions that aim to solve these questions: Sentence Transformers  [1] : an embedding generation technique based on textual information and; Qdrant : a vector database capable of storing embeddings and providing an easy interface to query them. These tools are applied to NPR [2], a News Portal R...
Read more

NIH autism database announcement raises concerns among researchers

Image
A new research initiative from the U.S. National Institutes of Health (NIH) seeks to gather and analyze multiple autism-related datasets to better understand the underlying causes of the condition. Letters of intent are due tomorrow, but the project’s atypical call for applications has autism scientists divided on whether or not to participate. The Autism Data Science Initiative provides $50 million for an anticipated 10 to 25 projects, according to the 27 May announcement. The funding opportunity uses an Other Transaction (OT) funding mechanism, which tends to be implemented when, among other reasons, a project is expected to evolve over time, more collaboration is required from the NIH or nontraditional research groups are involved, according to an NIH website. For a field that has recently seen budget cuts, some researchers are welcoming the opportunity for funding, says   Helen Tager-Flusberg , director of the Center for Autism Research Excellence at Bosto...
Read more

debunking it better with the Database of Known Fakes

Image
  Verifying online content can be a long and tiring process, and recent advances in AI have made it even easier to create and spread false and misleading texts, images and videos. Rushing to the rescue are tools that help journalists and fact-checkers verify such information, one of which is the Database of Known Fakes (opens in new window) . Called DBKF for short, the database was developed as part of an earlier EU project called WeVerify (opens in new window) . Now, it has been further enhanced under the EU-funded vera.ai (opens in new window) project. The DBKF’s main purpose is to help professionals such as journalists, open-source intelligence investigators and human rights defenders save time and be more efficient in their fact-checking and debunking tasks. Has a claim, image or video already been debunked by trusted, IFCN-approved (opens in new window) fact-checkers? Users can now easily verify this, and also see who did it, when and how it was done. “Leveraging text analys...
Read more