ReactiveFnJ facilitates in designing fully automated pipelines for distributed data processing systems, satisfying the Serverless Trilemma in true essence. The ReactiveFnJ handles embarrassingly parallel computations, and its design does not depend on any external orchestration services, messaging services, and queue services. As a Proof-of-Concept (PoC), the prototypical implementation of Split-Sort-Merge use case, based on Fork-Join workflow is discussed and evaluated. Our design uses two innovative patterns, namely, Relay Composition and Master-Worker Composition to solve execution time-out challenges. The intent of this work is to illustrate a design which is completely choreographed, reactive, asynchronous, and represents a dynamic composition model for serverless applications. To address this gap, we propose a fully serverless and scalable design model ReactiveFnJ for Fork-Join workflow. Some serverless orchestration systems exist, but they are in their primitive state and do not provide inherent support for non-trivial workflows like, Fork-Join. For building complex serverless applications, function composition is crucial to coordinate and synchronize the workflow of an application. In particular, our scheme shows more excellent performance when it comes to storing a large number of financial services multimedia images.įunction-as-a-Service (FaaS) is an event-based reactive programming model where functions run in ephemeral stateless containers for short duration. The experimental results show that our scheme achieves 2x-5x\documentclass lower than some popular image encryption schemes on time consumption, and simultaneously protects the security of images.
Specifically, for privacy-sensitive financial services images, we use the privacy-preserving scheme we proposed to store the privacy parts on different servers, and directly outsource the storage of the remaining images without sensitive information. Then we perform different operations on the two types of images based on our proposed privacy-preserving financial services image storage architecture. First, we use the EfficientDet neural network model to identify a large number of financial services images with sensitive data and classify the images. In this paper, we focus on this issue and propose a lightweight data storage approach for privacy-preserving financial services images in the cloud. Although data security storage has attracted great attention from researchers, the protection of massive amounts of data, especially for financial services images, is brought to a new level with the advent of the era of artificial intelligence. However, due to outsourcing the financial services image data to store on the cloud server, local users lose their direct control ability, which threatens the privacy of users.
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For local users with limited resources including enterprises and individuals, it is a better choice to make full use of the rich storage capacity of the cloud. With the development of multimedia technology and applications in the financial services industry, a large amount of multimedia image data related to financial services has been generated.