Investigating Methods to Enhance Data Privacy in Business, Especially in sectors like Analytics and Finance

Abstract

In today’s digital era, the issue of data privacy remains one of the most burning concerns for companies, especially in such fields as analytics and finance which deal with the collection and processing of big amounts of clients’ sensitive information. The present study seeks to explore approaches that can be used to improve data privacy, with a clear emphasis of Differential Privacy, anonymization, and Federated learning. They seek to give individual privacy in the handling and use of their data while enabling organizations to gain insights on the data. The research focuses on the influence of the laws, such as GDPR and CCPA, in defining and enforcing privacy requirements to progressively impact the organization’s operations. In respect of this, the study, which employs qualitative analysis and case studies from the finance and analytics industry, assesses the applicability of these approaches in minimizing risks of data breaches as well as enhancing consumer trust. Thus, the research indicates that the addition of PETs to regulation acts as a viable method for increasing privacy protection, while still having the issue of cost for the implementation of new technology in terms of money and organizational change. Also, the study reveals that there is always a gap that requires individuals to adjust practice, new privacy threats, and other changes in the regulations. In conclusion of this research, the need to adopt advanced technology as a tool for business development should be married with approaches that safeguard privacy. This paper establishes that optimization of protective measures is required to advance data protection in business scenarios especially regarding sensitive financial and analytical information, recommending integration of advanced technologies with a concrete legal backing.