Multidimensional Privacy Preservation in Distributed Computing and Big Data Systems: Hybrid Frameworks and Emerging Paradigms

Abstract

Distributed computing along with big data systems revolutionized how different industries handle and manage data. These systems are thus able to handle huge volumes of data with efficiency, promoting innovation even in such areas as health and finance. However, this technological advancement also causes significant privacy challenges. The nature of these systems, with the scale and heterogeneity of big data, presents points of vulnerability that can be exploited by malicious actors. Key issues include data breaches, unauthorized access, and the challenge of providing users with anonymity in large-scale environments. This paper discusses privacy concerns that are inherent to distributed computing and big data systems and underlines the urgent need for effective security mechanisms. It examines contemporary approaches to encryption, data anonymization, and secure multi-party computation to highlight strengths and weaknesses of the current approaches. It also points out the deficiency in some of the present research works and gives weight to the development of extensive privacy-preserving frameworks, which will guarantee the security of data handling, thus fostering trust and enabling further growth of distributed computing and big data applications.