A Randomized Kernel-Based Secret Image Sharing Scheme
This addresses the need for secure and efficient image sharing in applications like data protection, though it appears incremental as it builds on existing threshold schemes with kernel optimizations.
The paper tackles the problem of securely sharing secret images by proposing a (k,n)-threshold scheme using a randomized kernel operation, achieving flexible trade-offs between security and storage efficiency with share sizes at most equal to the secret image and no single point of failure.
This paper proposes a ($k,n$)-threshold secret image sharing scheme that offers flexibility in terms of meeting contrasting demands such as information security and storage efficiency with the help of a randomized kernel (binary matrix) operation. A secret image is split into $n$ shares such that any $k$ or more shares ($k\leq n$) can be used to reconstruct the image. Each share has a size less than or at most equal to the size of the secret image. Security and share sizes are solely determined by the kernel of the scheme. The kernel operation is optimized in terms of the security and computational requirements. The storage overhead of the kernel can further be made independent of its size by efficiently storing it as a sparse matrix. Moreover, the scheme is free from any kind of single point of failure (SPOF).