![A compressed two-layer privacy protocol demonstrates an inherent trade-off between data privacy and utility, revealing that minimizing the privacy parameter [latex]\varepsilon[/latex] to approximately 13-achieved with nine decoys-necessitates operating at a signal-to-noise ratio near one, where the signal is comparable to noise, and results in only a marginal 0.3% reduction in prior uncertainty as measured by normalized mean-squared error.](https://arxiv.org/html/2603.22808v1/x1.png)
Researchers have developed a novel framework, PolyVeil, that leverages geometric principles to securely combine private data streams without relying on conventional cryptographic methods.
A new approach to neural network design, leveraging Boolean logic instead of continuous values, dramatically improves resilience to bit-flip errors in parameters.
New research explores how combining robustness and uncertainty quantification offers a more comprehensive approach to evaluating the dependability of machine learning models.
![The modified [latex]\mathsf{Mult}.\mathsf{Online}[/latex] subroutine processes signals according to a defined flow, with the time index τ intentionally omitted to enhance clarity and focus on the core computational steps.](https://arxiv.org/html/2603.22857v1/x1.png)
A new protocol enables secure and efficient outsourcing of control computations using cryptographic techniques.
Modern software relies heavily on cryptography, and keeping those systems secure requires a proactive, architecture-driven approach to managing and migrating cryptographic components.
New research reveals that fully homomorphic encryption, despite its promise of secure computation, is surprisingly susceptible to subtle data corruption arising from hardware faults.
A new approach combines quantum key distribution with model predictive control to enhance privacy and reduce computational demands in networked control applications.
![The system, subjected to a simulated shock, initiates a safeguard mode as cross-system trust [latex]\bar{\lambda}(t)[/latex] diminishes, while the Q-GARS mechanism effectively contains queue backlog surges and rapidly restores capacity to near-zero levels, demonstrating resilience under stress.](https://arxiv.org/html/2603.23127v1/fig5.png)
A new framework leverages the principles of quantum optimization to enhance the speed and reliability of microservice deployments across cloud and edge environments.
As artificial intelligence systems increasingly rely on learned memories, a new vulnerability emerges: the deliberate corruption of those memories, with potentially devastating consequences.
A new runtime system, Accompanist, leverages decentralized sagas to improve the performance and scalability of microservice architectures.