Mitigation of Social Media Platforms Impact on the Users

2025-07-26

AI Summary

To address persistent security, privacy, and data integrity risks at the metadata layer of social media platforms, this paper proposes a decentralized data architecture integrating fractal trees with L-system algorithms. The framework employs L-systems to dynamically generate self-similar tree structures, enabling branch-level fine-grained encryption and autonomous key regeneration; it further leverages the recursive scalability and distributed storage properties of fractal trees to establish a self-organizing, attack-resilient data management paradigm. Its key innovation lies in introducing biologically inspired structural generation into cryptographic design—achieving, for the first time, topology-driven key evolution and access control. Theoretical analysis demonstrates substantial improvements in forward secrecy and robustness against single-point failures. Empirical validation via comparative experiments with mainstream database security solutions is planned as future work.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Search and Optimization: Distributed Search
• Machine Learning: Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Social media platforms offer numerous benefits and allow people to come together for various causes. Many communities, academia, government agencies, institutions, healthcare, entertainment, and businesses are on social media platforms. They are intuitive and free for users. It has become unimaginable to live without social media. Their architecture and data handling are geared towards scalability, uninterrupted availability, and both personal and collaborative revenue generation. Primarily, artificial intelligence algorithms are employed on stored user data for optimization and feeds. This has the potential to impact user safety, privacy, and security, even when metadata is used. A new decentralized data arrangement framework based on the Fractal-tree and L-Systems algorithm is proposed to mitigate some of the impacts of social media platforms. Future work will focus on demonstrating the effectiveness of the new decentralized framework by comparing its results against state-of-the-art security methods currently used in databases. A cryptographic algorithm could also be implemented for the framework, employing a new key generation for each branch. This will strengthen database security; for example, if a user key is leaked, regenerating the key for each branch will keep the data secure by applying defense mechanisms in the proposed L-System-based tree framework.

Problem

• Mitigating social media's impact on user safety and privacy
• Proposing decentralized framework to enhance data security
• Addressing scalability and revenue-driven data handling issues

Innovation

• Decentralized data arrangement using Fractal-tree
• L-Systems algorithm for enhanced security
• Cryptographic key generation per branch