Analyzing Privacy and Security in Cloud Computing Environments

Abstract

With the rapid surge of spam across the Internet and its various forms, effectively identifying and combating spam has become an urgent priority. Cloud computing offers significant advantages in terms of storage and processing capabilities, making it a viable solution for analysing vast amounts of email data. To address the dynamic nature of spam and its life cycle, an anti-spam system with feedback reassessment is proposed. This system incorporates a text filtering approach based on active learning, involving four key stages: training, filtering, feedback, and re-filtering. Compared to traditional systems, the feedback-enabled filtering system demonstrates improved keyword filtering. To further enhance the accuracy of spam detection and minimize misjudgements in legitimate emails, leveraging weighted decision-making based on email header information is recommended. Additionally, for emails with sparse content, employing title weighting in the filtering process proves to be both feasible and effective, particularly in identifying spam with minimal text content. Given the advancements of cloud-based filtering methods over traditional algorithms, leveraging cloud computing holds promise in effectively combating the escalating volume of spam. As such, this paper delves into an in-depth exploration of spam identification within cloud computing environments, focusing on text filtering systems. This study is informed by a comprehensive analysis of existing anti-spam technologies, aiming to contribute to the ongoing efforts in mitigating spam proliferation online.