Mitigation of Social Media Platforms Impact on the Users

TL;DR

This paper discusses the widespread use of social media, its associated risks to user safety and privacy, and proposes a novel decentralized data framework based on fractal trees and L-Systems to mitigate these impacts.

Contribution

It introduces a new decentralized data arrangement framework utilizing Fractal-tree and L-Systems algorithms to enhance security and privacy on social media platforms.

Findings

Proposes a decentralized framework for social media data security.

Plans to compare the framework's effectiveness against current security methods.

Suggests implementing cryptographic algorithms with dynamic key generation.

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.