Data Security and Sensitive Data Protection using Privacy by Design Technique

Abstract

Adhering to internationally accepted data privacy principles in every new data analytics initiative ensures compliance with virtually all current government or industry-mandated data privacy regulations and enables the business to extract maximum financial benefit from available data assets without violating the trust between the organization and its customers. Privacy by design is a fundamentally important approach to achieving compliance with GDPR, HIPAA, and other data privacy or data protection regulations. This paper outlines how organizations can save time and money while improving data security and regulatory compliance and dramatically reduce the risk of a data breach or expensive penalties for noncompliance. What privacy by design means? Why it’s so important? and how to implement it within any organization, using data protection and data access control technologies. Build it in from the beginning to facilitate better regulatory compliance, reduce risk, reduce operating costs, shorten development times, improve customer trust and loyalty, and gain more open access to sensitive and regulated data for business benefit—without violating generally accepted privacy principles. The big change is Big Data. All the more explicitly, how organizations will use Big Data analytics to boost these developing data resources – driven by their profound enthusiasm to augment their assets and better contend in the market. While organizations have down to earth motivations to take advantage of their consistently developing perception space, they additionally have a squeezing need to implant in these frameworks improved security assurances. We outline in this paper just such an example – how an advanced Big Data sensemaking technology was, from the ground up, engineered with privacy-enhancing features. Some of these features are so critical to accuracy that the team decided they should be mandatory – so deeply baked-in they cannot be turned off. This paper demonstrates how privacy and responsibility can be advanced in this new age of Big Data analytics.