@Article{cmc.2026.088561,
AUTHOR = {Yongfeng Zhang, Jie Chen},
TITLE = {Privacy-Preserving Edge Intelligence for Speaker Verification via Uncertainty-Aware Adaptive Feature Offloading},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {89},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v89n2/68845},
ISSN = {1546-2226},
ABSTRACT = {Speaker verification on phones, wearables, and voice-enabled Internet-of-Things gateways must balance local privacy with reliable decisions under adverse audio. Existing privacy-preserving verification schemes generally protect a fixed representation, whereas edge-offloading policies usually adapt computation without attaching an explicit feature-disclosure budget; neither line alone coordinates trial uncertainty, communication state, and privacy expenditure. This paper presents privacy-preserving uncertainty-aware adaptive feature offloading (P-UAFO), an edge-intelligence framework that keeps raw audio local and transmits only clipped, projected, quantized, and Gaussian-perturbed intermediate features when their expected benefit justifies resource cost. Its online pipeline first estimates decision uncertainty and resource state, filters infeasible actions, selects local, low-tier, or high-tier processing, and fuses the returned score only when cloud evidence is sufficiently reliable. We establish a feature-level differential-privacy guarantee, an uncertainty threshold for offloading, a variance-optimal fusion rule, and a bounded-regret result under prediction error. The evaluation uses reproducible controlled events covering clean/noisy conditions, 0.4–12 Mb/s uplinks, 20–90 ms round-trip delay, and perturbed resource predictions. The results validate the controller logic and its practical ability to avoid unnecessary feature transmission; they do not constitute a public-corpus or mobile-device benchmark, which remains necessary before real-world deployment claims can be made.},
DOI = {10.32604/cmc.2026.088561}
}