English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Efficiently processing medical images, such as whole slide images in digitalpathology, is essential for timely diagnosing high-risk diseases. However, thisdemands advanced computing infrastructure, e.g., GPU servers for deep learninginferencing, and local processing is time-consuming and costly. Besides,privacy concerns further complicate the employment of remote cloudinfrastructures. While previous research has explored privacy andsecurity-aware workflow scheduling in hybrid clouds for distributed processing,privacy-preserving data splitting, optimizing the service allocation ofoutsourcing computation on split data to the cloud, and privacy evaluation forlarge medical images still need to be addressed. This study focuses ontailoring a virtual infrastructure within a hybrid cloud environment andscheduling the image processing services while preserving privacy. We aim tominimize the use of untrusted nodes, lower monetary costs, and reduce executiontime under privacy, budget, and deadline requirements. We consider a two-phasesolution and develop 1) a privacy-preserving data splitting algorithm and 2) agreedy Pareto front-based algorithm for optimizing the service allocation. Weconducted experiments with real and simulated data to validate and compare ourmethod with a baseline. The results show that our privacy mechanism designoutperforms the baseline regarding the average lower band on individual privacyand information gain for privacy evaluation. In addition, our approach canobtain various Pareto optimal-based allocations with users' preferences on themaximum number of untrusted nodes, budget, and time threshold. Our solutionsoften dominate the baseline's solution and are superior on a tight budget.Specifically, our approach has been ahead of baseline, up to 85.2% and 6.8% interms of the total financial and time costs, respectively.

Towards Privacy-, Budget-, and Deadline-Aware Service Optimization for Large Medical Image Processing across Hybrid Clouds