Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3, 1972-1985 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 17 January 2025; Revised: 21 March 2025; Accepted: 21 March 2025; Published: 24 March 2025 * Correspondence: harryqi77@163.com Research on privacy protection and data security policy optimization of digital copyright blockchain platform Qi Qi1*, Lea Grace B. Salcedo2 1,2College of Business Administration, University of the Cordilleras, Gov. Pack Road, Baguio City, the Philippines, 2600; harryqi77@163.com (Q.Q.) lbsalcedo@uc-bcf.edu.ph (L.G.B.S.). Abstract: This research investigates privacy protection mechanisms and data security policy optimization for blockchain-based digital rights management platforms to balance transparency with robust privacy protection. A comprehensive experimental framework was developed, integrating advanced cryptographic techniques with intelligent policy management systems. A multi-layered validation methodology employed formal verification, black-box/white-box testing, and stress tests to validate performance across security, efficiency, and usability dimensions. The implemented solution provided 99.99% security assurance while achieving a 47% improvement in processing efficiency through zero-knowledge proofs and homomorphic encryption. Transaction processing reached 3,750 TPS (peaking at 4,200 TPS), with 99.8% regulatory compliance and 99.9% automated policy conflict resolution. The research demonstrates significant advancements in blockchain-based privacy protection through novel cryptographic implementation and automated policy management, establishing a robust framework for secure digital rights management. This solution offers substantial value for content delivery networks, digital asset management systems, financial institutions, and government services where the balance between transparency and privacy is critical, while reducing compliance management costs. Keywords: Blockchain, Digital rights management, Policy optimization, Privacy protection, Zero-knowledge proofs. 1. Introduction Because the blockchain technology is developing so fast nowadays, it plays a significant role in digital rights management and data security systems. Blockchain is quite an appealing solution for the security of digital assets and the protection of privacy due to its inherent characteristics: decentralized, tamper-proof, and transparent [1]. However, in the process of maturity, how to balance transparency with protection in blockchain-based digital rights management systems has gradually become a key issue. The integration of blockchain technology in enterprise environments has introduced new paradigms for securing sensitive data and managing digital rights [2]. Recent studies have highlighted the critical need for robust privacy preservation models, particularly in cloud-based environments where data security concerns are paramount [3]. The emergence of mobile identity protection frameworks has further emphasized the importance of developing comprehensive security solutions that can adapt to evolving technological landscapes [4]. Among such applications, government services stand out regarding the adoption rate of blockchain platforms. Several initiatives have demonstrated the blockchain potential to improve public sector operations: such examples, including Dubai government adoption of blockchain services, already provided real-world reference models of secure and effective digital governance [5]. These implementations have shown that formal verification approaches play a significant role in the context of smart contract security [6] especially in very critical applications, such as digital voting systems [7]. Implementation of blockchain-based data storage systems has drawn wide interest for developing more sophisticated information systems management frameworks because it has initially shown organizational performance improvements in government agencies [8, 9]. Integration of Zero-trust architectures and blockchain, mailto:harryqi77@163.com mailto:lbsalcedo@uc-bcf.edu.ph 1973 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate therefore, came up as a promising way to advance edge computing security [10]; hence, the development of innovative service function chaining mechanisms [11]. Standardization of smart contracts [12] and multi-layer blockchain protocols for identity authentication [13] represent major strides toward resolving the user acceptance problems highlighted within the more conventional technology adoption models [14]. Applications of these blockchain features have been used to power transmission systems and security-related data exchange [15] an indication that the spheres of its applications are varied. Recent advances in trusted computing [16] and decentralized trust management systems [17] have further led to even more advanced self-sovereign identity management solutions [18]. Non-transferable blockchain-based identity authentication mechanisms have been developed that resolve many of the critical security issues without any sacrifice regarding system usability [19]. It has had quite an important impact on organizational security practices, with a balance of trust and protective structures still influential in shaping employee behavior [20]. The convergence of blockchain technology with existing mobile SNS trust models [21] has created new opportunities for enhancing government trust through improved media responsiveness [22]. Information security risk assessments in critical infrastructure systems have benefited from blockchain integration [23] leading to more robust and reliable security frameworks. Healthcare sector applications have demonstrated the potential of intelligent computing techniques in blockchain implementation [24] while financial services have seen improvements in digital identity solutions [25]. This research work points to important challenges in blockchain-based protection of privacy and in the optimization of data security policy for digital rights management platforms. Based on the analysis of existing implementations and by providing novel solutions, this study is supposed to further contribute to the evolution of secure digital rights management while keeping the difficult balance between transparency and protection of privacy. 2. Research Methods 2.1. Theoretical Foundation The concept of blockchain-based privacy protection leverages several critical frameworks from cryptography and distributed systems. The sustainable supply chain theory [26, 27] highlights some important insights in the adoption barriers regarding the application of blockchain technology, while the trust management strategies for digital twins [28] provide a fine-grained view for network security. Since the advent of blockchain technology, its use has shifted from cryptocurrencies to maintaining authentic data, digital governance frameworks, and much more. For instance, Liu, et al. [29] presented BTDSI, which is a blockchain-based trusted data storage model tailored for Industry 5.0 environments. It overcomes the issues of data non-repudiation, authenticity and privacy preservation. This is in tandem with organisational governance perspectives explored by Lumineau, et al. [30] who investigated how blockchain technology radically reverts primitive governance systems by lessening asymmetry of information and increasing reliance on information computed consensus mechanisms. These advancements also apply to the public sector. As Lykidis, et al. [31] showed, blockchain technology used in e- government services is beneficial in enhancing transparency while protecting citizens' private information through discretionary disclosure policies. Furthermore, the security implications are broadened when threat intelligence ecosystems are considered. Nazir, et al. [32] showed that using blockchain and machine learning in a collaborative approach creates guards around the IoT security posture without divulging sensitive information about the entities participating in the collaboration. The integration of blockchain with IoT gave way to the development of frameworks dealing with self-sovereign identity management [33] and forms a basis for one of the key theoretical pillars in this research. Identity-based authentication protocols have evolved to incorporate zero-knowledge proof and homomorphic encryption, which can enable privacy-preserving transactions while maintaining system integrity [34, 35]. The theoretical underpinning of mobile social networks' privacy protection [36] complements the three-factor authentication framework [37] providing a comprehensive foundation for secure digital rights management. Recent advances in blockchain-based government services [38] have contributed to the theoretical understanding of privacy preservation in public sector applications. This research synthesizes these theoretical frameworks while incorporating emerging concepts in biometric authentication [39] and sentiment analysis [40] to establish a robust theoretical basis for privacy protection in blockchain-based digital rights platforms. 2.2. Research Design 2.2.1. Experimental Environment Design The experimental environment for this research has been carefully constructed to ensure comprehensive testing of the proposed privacy protection mechanisms. Building upon existing blockchain security frameworks 1974 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate [41] we established a multi-layered testing environment that incorporates both physical and virtual components. The infrastructure leverages industrial internet security principles [42] and follows the formalized requirements for decentralized applications [43]. As shown in Table 1, the experimental environment encompasses various hardware and software configurations designed to simulate real-world conditions. The testing infrastructure includes high-performance computing nodes, specialized cryptographic processing units, and network simulation tools to ensure accurate performance measurement [44]. Table 1. Experimental Environment Specifications. Component Configuration Specifications Purpose Processing Nodes High-Performance Servers Intel Xeon E5-2690 v4, 128GB RAM Blockchain Node Operation Storage System Distributed Storage 10TB SSD Arrays, RAID 10 Data Management Network Infrastructure Enterprise Grade 10Gbps Backbone, <2ms Latency Network Simulation Security Modules Hardware Security TPM 2.0, HSM Integration Cryptographic Operations Virtualization Platform Cloud-Native Kubernetes v1.24, Docker 20.10 Container Management Monitoring Systems Real-time Analytics Prometheus, Grafana Performance Tracking 2.2.2. Evaluation Metrics Framework The evaluation metrics framework incorporates comprehensive performance indicators derived from cutting- edge research in network security [45] and blockchain-based authentication systems. Our assessment methodology encompasses both technical and user-centric metrics, focusing on security, efficiency, and usability aspects. As detailed in Table 2, the evaluation framework is structured to capture both quantitative and qualitative measures of system performance. Table 2. Evaluation Metrics Framework. Category Metric Measurement Method Target Value Weight Security Performance Privacy Protection Level Zero-knowledge Proof Validation >99.9% 0.30 System Efficiency Transaction Throughput TPS Measurement >3000/s 0.25 Response Time API Latency End-to-End Testing <100ms 0.20 Resource Utilization System Load Resource Monitoring <70% 0.15 User Experience Satisfaction Score User Surveys >4.5/5 0.10 2.2.3. Data Collection Methods The data collection methodology employs a comprehensive approach integrating multiple data sources and collection techniques based on established research in mobile payment systems and IoT device authentication. Our approach emphasizes the importance of data quality and reliability while maintaining privacy standards throughout the collection process. As shown in Table 3, we implemented various data collection methods across different system components and user interactions. Table 3. Data Collection Methods and Sources. Data Type Collection Method Sample Size Collection Frequency Validation Method System Logs Automated Logging 1M entries/day Real-time Hash Verification User Transactions Blockchain Events 100K/day Per Block Consensus Validation Performance Metrics System Monitoring 5K samples/hour Continuous Statistical Analysis Security Events Alert System As Occurred Real-time Manual Review User Feedback Survey System 1000 responses Monthly Cross-validation 2.3. Validation Method The validation methodology hereby constitutes a multi-layer verification approach: three layers. In order to make the proposed mechanism reliable and effective for privacy protection, there will be formal mathematical verification of cryptographic protocols using some automated theorem-proving tools, which gives rigorous proof of security properties. Verification is based on state-of-the-art static and dynamic analysis techniques that put 1975 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate particular emphasis on validation with zero-knowledge proofs and verification of homomorphic encryption. In the system integration layer, we applied both black-box and white-box testing methods. In black-box testing, we check the behavior of the system from the outside by observing its responses to different input conditions without knowledge about internal implementation. On the contrary, white-box testing allows for detailed inspection of the internal logics and data flow, ensuring that, at each processing stage, privacy protection mechanisms work as intended. The performance validation is performed by a set of stressing tests and intrusion scenarios probing for the robustness of the system in maximal load conditions. These tests are run to simulate a variety of attack vectors and high-load situations: measurement of system response times, resource utilization, and effectiveness of privacy protection. Further validation is done by cross-validation with existing benchmarks and recognized standards, which ensures the reliability of our results. These data were further analyzed with the use of both parametric and non-parametric statistical methods to take into account the distribution of anomaly data, thus ensuring that our results are statistically significant. 3. Results of the Study 3.1. Blockchain Platform Privacy Protection Mechanism Experimental Results 3.1.1. Cryptographic Scheme Performance Evaluation The experimental results demonstrate exceptional performance in the implemented cryptographic schemes across multiple dimensions. The zero-knowledge proof implementation achieved significant improvements in verification speed and computational efficiency compared to traditional approaches. Performance metrics indicate a 47% reduction in processing overhead while maintaining a 99.99% security assurance level. As shown in Table 4, the homomorphic encryption scheme exhibited remarkable performance in both encryption and decryption operations, with particular efficiency in batch processing scenarios. Table 4. Cryptographic Performance Metrics Analysis. Cryptographic Method Processing Time (ms) Security Level Resource Usage (%) Success Rate (%) Zero-Knowledge Proof 12.3 AES-256 23.5 99.99 Homomorphic Encryption 18.7 RSA-3072 31.2 99.97 Ring Signatures 15.4 ECC-384 27.8 99.95 Threshold Signatures 14.2 DSA-3072 25.6 99.98 Blind Signatures 16.8 Ed25519 29.3 99.96 3.1.2. Privacy Data Processing Effects The privacy data processing mechanism demonstrated robust performance in maintaining data confidentiality while ensuring operational efficiency. Our analysis reveals significant improvements in data anonymization quality and processing speed. The system achieved optimal balance between privacy preservation and data utility, as evidenced by the comprehensive metrics shown in Table 5. The processing pipeline maintained consistent performance even under high-load conditions, with negligible impact on system responsiveness. Table 5. Privacy Data Processing Performance Results Processing Stage Anonymization Level Processing Speed (MB/s) Data Utility (%) Error Rate (%) Data Ingestion Level 3 256.4 94.5 0.05 Transformation Level 4 198.7 92.8 0.08 Storage Level 5 312.3 96.2 0.03 Retrieval Level 4 287.9 95.7 0.04 Distribution Level 5 245.6 93.9 0.06 3.1.3. Access Control Mechanism Verification The access control mechanism verification results indicate superior performance in managing and enforcing access policies across the blockchain platform. The implemented system demonstrated robust capability in handling complex access scenarios while maintaining strict security protocols. The verification process revealed 1976 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate exceptional accuracy in permission management and policy enforcement, as detailed in Table 6. The system successfully prevented unauthorized access attempts while maintaining efficient processing of legitimate requests. Table 6. Access Control Verification Results. Control Mechanism Response Time (ms) Authorization Accuracy (%) False Positive Rate (%) Security Score Role-based Access 8.4 99.97 0.02 9.8/10 Attribute-based Control 10.2 99.95 0.03 9.7/10 Context-aware Access 12.7 99.93 0.04 9.6/10 Multi-factor Auth 15.3 99.99 0.01 9.9/10 Smart Contract Enforced 9.8 99.96 0.02 3.2. Comprehensive Assessment of Safety Performance 3.2.1. System Security Testing The comprehensive system security testing revealed robust performance across multiple security dimensions, with particular strength in attack resistance and system resilience. The testing protocol encompassed various attack vectors including DDoS attempts, SQL injection attacks, and man-in-the-middle interventions. As shown in Figure 1, the system demonstrated exceptional performance in threat detection and mitigation, with a 99.97% success rate in identifying and neutralizing potential security threats. The response time for threat detection averaged 2.3 milliseconds, significantly outperforming conventional security systems. The analysis of security incidents over a six-month period indicates a consistent decline in successful breach attempts, while the system's adaptive learning mechanisms showed continuous improvement in threat recognition patterns. Figure 1. System Security Performance Metrics. 3.2.2. Privacy Protection Strength Analysis The privacy protection strength analysis demonstrates exceptional effectiveness in maintaining data confidentiality while ensuring system accessibility. The implemented privacy mechanisms showed remarkable 1977 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate capability in preventing unauthorized data access attempts while maintaining optimal performance for legitimate users. As illustrated in Figure 2, the privacy protection index maintained consistently high levels across different data categories and access scenarios. The analysis reveals a significant improvement in privacy preservation metrics, with an average protection strength of 96.8% across all tested scenarios. Figure 2. Privacy Protection Strength Trends by Data Category. 3.2.3. Security Policy Implementation Effects The evaluation of security policy implementation effects reveals significant improvements in policy enforcement efficiency and accuracy. The implemented security policies demonstrated robust performance in maintaining system integrity while ensuring smooth operational flow. As depicted in Figure 3, the policy execution effectiveness showed consistent enhancement over the implementation period, with particularly strong performance in automated policy enforcement and real-time adaptation to security threats. 1978 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate Figure 3. Security Policy Implementation Effectiveness. 3.3. System Performance Index Evaluation 3.3.1. Transaction Processing Performance The transaction processing performance analysis reveals significant improvements in throughput and latency metrics across the blockchain platform. Our system achieved a remarkable average transaction processing speed of 3,750 transactions per second (TPS), representing a 42% improvement over conventional blockchain implementations. As shown in Figure 4, the processing capacity demonstrated consistent stability under varying load conditions, with peak performance reaching 4,200 TPS during high-demand periods. The average confirmation time for transactions maintained at 2.3 seconds, with a 99.9% success rate in transaction validation. The performance metrics indicate exceptional scalability, with the system maintaining optimal efficiency even as transaction volumes increased by 300% during stress testing periods. 1979 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate Figure 4. 24-Hour Transaction Processing Performance. Analysis: 24-hour monitoring of system transaction processing capacity, showing consistent high performance with peak efficiency during peak usage periods. 3.3.2. Resource Utilization Efficiency The analysis of resource utilization efficiency demonstrates optimal allocation and management of system resources across the blockchain network. Our monitoring revealed an average CPU utilization rate of 67%, with memory usage stabilizing at 72% during peak operational periods. As illustrated in Figure 5, the resource consumption patterns show efficient scaling with workload variations, maintaining a balanced distribution across network nodes. The system achieved a 28% improvement in resource efficiency compared to baseline measurements, while supporting a 45% increase in concurrent user sessions. 1980 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate Figure 5. System Resource Utilization Over 24 Hours. Daily Resource Utilization Patterns: Comparative analysis of CPU and memory usage patterns over a 24-hour period, demonstrating efficient resource management and utilization scaling. 3.4. Evaluation of Policy Optimization Effect 3.4.1. Compliance Verification Results The compliance verification results demonstrate exceptional adherence to regulatory requirements and industry standards across all evaluated dimensions. Our comprehensive assessment revealed a 99.8% compliance rate with GDPR requirements and a 99.7% alignment with CCPA regulations. As shown in Table 7, the system achieved significant improvements in compliance metrics across various regulatory frameworks. The verification process encompassed multiple compliance domains, including data privacy, security protocols, and user rights management. The implementation of automated compliance monitoring mechanisms resulted in a 43% reduction in compliance-related incidents and a 67% improvement in response time to regulatory changes. The system's adaptive compliance framework demonstrated particular strength in cross-jurisdictional scenarios, maintaining consistent performance across different regulatory environments. Table 7. Compliance Verification Performance Metrics. Regulatory Framework Compliance Rate (%) Implementation Level Risk Score Audit Success Rate (%) Response Time (hours) GDPR 99.8 Advanced 0.2 99.9 1.2 CCPA 99.7 Advanced 0.3 99.8 1.4 ISO 27001 99.9 Complete 0.1 100 1.0 SOC 2 99.6 Advanced 0.4 99.7 1.5 HIPAA 99.8 Complete 0.2 99.9 1.3 PCI DSS 99.9 Complete 0.1 100 1.1 1981 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate 3.4.2. Policy Implementation Effects The assessment of policy implementation effects reveals substantial improvements in both efficiency and effectiveness of security policy enforcement. The implementation of automated policy management mechanisms resulted in a 78% reduction in policy conflicts and a 92% improvement in policy deployment speed. As detailed in Table 8, the policy execution metrics demonstrate significant enhancements across all key performance indicators. The system's ability to dynamically adjust policy parameters based on real-time threat analysis contributed to a 56% reduction in security incidents and an 89% improvement in threat response times. The automated policy reconciliation mechanism successfully resolved 99.9% of policy conflicts without manual intervention. Table 8. Policy Implementation Performance Analysis Policy Category Execution Rate (%) Conflict Resolution (%) Average Response Time (ms) Coverage Rate (%) Effectiveness Score Access Control 99.9 99.8 23 100 9.9/10 Data Protection 99.8 99.9 28 99.9 9.8/10 Network Security 99.9 99.7 18 100 9.9/10 Identity Management 99.7 99.8 25 99.8 9.7/10 Incident Response 99.8 99.9 21 99.9 9.8/10 Compliance Management 99.9 99.9 20 100 9.9/10 4. Discuss 4.1. Experimental Results Analysis A deep analysis of the test results reveals several findings about the effectiveness of the proposed blockchain- based private protection scheme in ensuring digital rights. The performances of cryptography schemes could achieve excellence in several keys: for instance, zero-knowledge proof could achieve 99.99% safety assurance in implementing a processing overhead of less than 25% in test results. This is explained by the fact that efficiency was given by an optimized implementation of the homomorphic encryption scheme that increased its processing rate by 47% compared to the traditional approach, while the mechanism of privacy data processing achieved quite a good trade-off between privacy and availability: average anonymization level of 4.8 of 5, with the utility preservation at 94.5%. The system security testing results indicate robust protection against various attack vectors, with a 99.97% success rate in threat detection and an average response time of 2.3 milliseconds. This performance significantly surpasses existing security solutions in both accuracy and speed. The privacy protection strength analysis revealed consistent performance across different data categories, with personal data protection achieving the highest score of 97.8%. It also performed the implementation of smart contract-enforced access control mechanisms very effectively: 99.96% of authorization accuracy, with very few false positives. The performance of transaction processing had significantly better metrics, while the system was supporting an average throughput of 3,750 TPS under normal conditions, turning peak loads as high as 4,200 TPS. This is a 42% improvement over traditional blockchain implementations while sustaining success rates over 99.9% in transaction validations. Resource utilization patterns showed optimum efficiency, with average CPU and memory use staying well within acceptable thresholds despite such a high load increase. Compliance verification was particularly impressive, reaching compliance with GDPR requirements at 99.8%, and similar high levels in other regulatory frameworks. The results in terms of policy implementation came out to be very astounding: 99.9% for automatic conflict resolution, meaning policy reconciliation with no human intervention. All these results combined hint at the robustness of the proposed solution for handling technical and regulatory requirements while providing high performance. 4.2. Innovation Solution Analysis A number of the most important innovative aspects of the research focus on: advancing the state-of-the-art in the area of blockchain-based privacy protection in a number of key areas; reducing adaptive zero-knowledge proofs to homomorphic encryption in a novel manner for balancing transparency with privacy requirements; and implementation of dynamic policy adjustment mechanisms based on real-time threat analysis-highly innovative in automated security management. A hybrid consensus mechanism, optimized for digital rights management, is a 1982 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate leap in scalability for blockchains with proven privacy. Smart contract-based policy enforcement introduces automated conflict resolution: this shows new ways in which the management of policy is being handled within distributed systems. Each of these adds to create a more robust and effective framework of protection of privacy against challenging issues regarding digital rights management in blockchain environments. 4.3. Application Value Assessment Besides that, this research has huge practical potential in several industries and domains regarding the use case of digital rights management. The implemented system already shows its applicability to content distribution platforms, such that creator rights are well protected and user privacy is guaranteed. This solution is even more valuable because it can be scaled up to large-scale digital asset management systems where otherwise traditional approaches will always face challenges with respect to performance versus privacy trade-offs. This will, in turn, equally enable financial institutions to exploit the strong compliance framework which auto-changes with each regulatory change without their losing operational efficiencies. Capable of handling a very large volume of transactions along with privacy, the system is rightly positioned to be deployed in Government services, Healthcare data management, and Intellectual Property Protection Systems. Demonstrated cost reduction in compliance management and improved operational efficiencies create clear economic benefits that make the solution attractive for commercial adoption. 4.4. Limitation Analysis Despite these considerable achievements, some limitations must be stated. The current version requires considerable computational resources to function optimally, and this therefore limits its possible deployment under resource-constrained conditions. High-bandwidth network connectivity is required, which might be a challenge in regions that do not have suitable infrastructure. The zero-knowledge proof system is complex to maintain and keep up to date; this requires specific expertise, raising the possible cost of operation. While the privacy protection mechanisms are strong, a little latency could be observed in very high-frequency trading applications where microsecond response times are critical. This is because, while the auto policy reconciliation system works very well, it may require human intuition in more complex regulatory landscapes where requirements conflict. These limitations-though minor and not affecting the overall value proposition-suggest avenues of future research in further enhancing the versatility and ease of access of the system. 5. Conclusion This paper presents a deep study in the field of privacy protection and optimization of data security policy in blockchain-based digital rights management platforms. We will also provide extensive experiments and detailed analysis to prove that the blockchain will enhance its privacy protection and security management in many essential aspects. The implemented system achieved very impressive performance metrics, where the privacy protection mechanism, while being able to allow the system to possess efficient processing capability, showed a 99.99% security assurance level. The integration of the advanced cryptographic scheme, including zero-knowledge proof and homomorphic encryption, enhanced the processing efficiency by 47% compared to the traditional approach. It reached 3,750 TPS during normal time and 4,200 TPS at the peak, which was a considerate advance in blockchain scalability. Our solution to innovating policy optimization and compliance management created unmatched results: 99.8% compliance against major regulatory frameworks such as GDPR and CCPA. Automation in the policy reconciliation system resolved 99.9% of the policy conflicts on its own, showing the efficiency of our intelligent policy management framework. Resource utilization patterns inside the system showed that it was highly efficient, with stable performance even under conditions of high load. Contributions from this research go beyond mere technical contributions into practical applications within diverse industry verticals. Its adaptability to different regulatory regimes, along with robust mechanisms of privacy protection, will make it especially attractive to industries involved in sensitive digital assets. In addition, such gains in operational efficiency together with reduced compliance management costs serve as compelling proof of the commercial viability of the solution. While admitting the limitation to some, especially those requiring high resources and specialized expertise, the overall results substantiate the efficiency in our approach toward blockchain-based privacy protection. The successful integration of advanced cryptographic techniques together with intelligent policy management systems develops a modern face in secure digital rights management. The future directions of research might rest on revising identified limitations, mainly in the optimization of resource requirements or increasing system accessibility. Further investigation in emerging cryptographic techniques and their integration with blockchain 1983 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1972-1985, 2025 DOI: 10.55214/25768484.v9i3.5732 © 2025 by the authors; licensee Learning Gate technology can provide further enhancement to the system. The provided proof of concept in the automation of policy management also opens new research paths in artificial intelligence-driven security policy optimization. The result from this work offers valuable lessons from the implementation of privacy-preserving blockchain systems and contributes to the overall understanding of secure digital rights management. The results therefore have important implications for both academic research and practical application in the field of blockchain security and privacy protection. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] S. Aggarwal and N. Kumar, Blockchain technology for secure and smart applications across industry verticals. 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