Secure and Parallel Determinant Computation for Large-Scale Matrices in Edge Environments
The advent of edge computing has enabled resource-constrained clients to delegate intensive computational tasks to distributed edge servers, especially within Internet of Things (IoT) environments. Among such tasks, Matrix Determinant Computation (MDC) remains critical for applications in control systems, cryptography, and machine learning. However, the cubic complexity of traditional determinant algorithms makes them unsuitable for real-time processing in constrained edge scenarios. We propose a Secure Parallel Determinant Computation (SPDC) framework, which provides strong security guaranties, including privacy-preserving MDC, across N distributed edge servers. The framework achieves pr...
| Authors | Prajwal Panth |
|---|---|
| Primary category | cs.DC |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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