StegoSpotify: A Novel Approach to Data Concealment Using Music Streaming Platforms and Linked List Structures

This paper presents StegoSpotify, a novel steganographic approach that leverages music streaming platforms as unconventional storage media for concealing arbitrary digital files. By mapping file bytes to music tracks through cryptographic hashing and implementing a linked list data structure across multiple playlists, this method achieves theoretically unlimited storage capacity while maintaining data integrity and confidentiality. The implementation utilizes the Go programming language for concurrent API operations, cryptographic seed generation for deterministic track selection, and rate-limiting strategies to circumvent platform restrictions. Experimental results demonstrate successful file encoding, storage, and retrieval with byte-level accuracy. This work explores the intersection of cloud-based media platforms, data structures, and modern steganography, highlighting both the technical feasibility and inherent limitations of using public streaming services as covert storage systems. The research contributes to the understanding of steganography without embedding (SWE) in networked environments and raises important considerations regarding platform reliability, data persistence, and security implications.