AI ANALYSIS REPORT

Our framework addresses these limitations by integrating four key components:

• Attribute-Based Access Control (ABAC): Provides fine-grained, contextualized access policies based on user, resource, and environmental attributes, offering greater flexibility than traditional models.
• Blockchain Network: A private/consortium blockchain stores access control policies and audit trails immutably and transparently, removing the possibility of undesired modifications and ensuring data integrity.
• Smart Contracts: Automated enforcement of ABAC policies in real-time within a trustless environment, reinforcing decentralization and eliminating human error.
• Upgraded Black-winged Kite (UBK) Algorithm: A novel metaheuristic optimization algorithm that streamlines ABAC policies by eliminating redundancy, resolving conflicts, and reducing computational overhead, improving decision-making accuracy.

This unique combination ensures a secure, scalable, and auditable access control system for dynamic geospatial environments.

The Upgraded Black-winged Kite (UBK) algorithm is a crucial innovation within our framework, designed to optimize ABAC policies and enhance system performance. Its key functions include:

• Policy Optimization: Translates ABAC policies into optimized rules by eliminating redundant and merging conflicting rules, significantly reducing computational overhead during policy evaluation.
• Enhanced Exploration and Exploitation: Incorporates Cauchy mutation and adaptive control parameters for a flexible and responsive search strategy, preventing premature convergence to local optima and ensuring robust policy evolution.
• Conflict Resolution: Automatically identifies and resolves conflicts between policies, ensuring consistent and accurate access decisions.
• Scalability Improvement: Streamlines policy management, making the system efficient and manageable even as the number of users, resources, and attributes grows.

Experimental results demonstrate that UBK significantly outperforms traditional metaheuristic algorithms like GA-CS and PSO in convergence speed and solution quality, achieving a 55% reduction in policy complexity.

The proposed system is engineered for robust scalability and security:

• Scalability: Performance tests show that policy evaluation time and storage overhead increase linearly with a growing number of users (up to 10,000) and resources (up to 5,000). This near-linear scalability makes it highly suitable for large-scale, real-world deployments.
• Decentralized Security: Utilizes a private or consortium blockchain with a Proof-of-Authority (PoA) consensus mechanism, providing low latency, high throughput, and permissioned participation for trusted entities.
• Tamper-Proof Data: Access control policies, audit logs, and dataset metadata are stored immutably on the blockchain, preventing unauthorized modifications.
• Data Integrity & Privacy: Sensitive attributes are stored on-chain in encrypted form, ensuring domain-security. Formal verification tools (Certora, Mythril) are integrated into smart contract development to banish logical and execution flaws.
• Auditable Trails: All access requests and decisions are transparently logged on the blockchain, enabling clear accountability and compliance verification.

These features collectively address critical security and scalability challenges in distributed data sharing.

Extensive experimentation, using MATLAB R2019b and a synthetic geospatial dataset of 10,000 records, validates the framework's superior performance:

• Policy Evaluation Time: A significant 70% reduction compared to traditional ABAC, and 46.4% reduction over GA-CS (0.15s vs 0.5s).
• Storage Overhead: A 52% reduction compared to traditional ABAC, and 66.7% reduction over GA-CS.
• Access Decision Accuracy: Achieved 98.2% accuracy, outperforming GA-CS (95.0%) and PSO (94.1%).
• UBK Optimization Efficiency: The UBK algorithm achieved the lowest objective function value (0.30) among tested metaheuristic algorithms (GA-CS, PSO, GWO, IHHO), indicating its ability to balance multiple objectives effectively and reducing policy complexity by 55%.
• Scalability Validation: Confirmed near-linear growth in evaluation time and storage for increasing users and resources, ensuring suitability for large-scale deployment.

These results affirm the framework's ability to deliver secure, efficient, and scalable geographic spatial data sharing.