Tesseract Optimization for Data Privacy and Sharing Economics

Abstract

As human beings, we have the inherent bias towards our likes and dislikes. These likes and dislikes are influenced by our psychographic attributes, morals, values, beliefs and societal networks in both circles of influence and circles of concern. We continuously consume products, services as well as produce products and services for others to consume. Due to our inherent bias of likes and dislikes, we unintentionally or intentionally end up liking products and/or services that are personalized for us, or somehow unintentionally match our likes. This personalization makes our lives easier and more comfortable as it may save time, and/or enable us to achieve desired experiences. This personalization is influenced by the data gathered about us directly or indirectly. The richer the data is about us, the more personalized products and services we receive, thereby saving us time and money whilst meeting our desired goals for experience or exchange of value for the offers we are wanting. We remain forever concerned about the direct or accidental use of our data that can fall into the hands of nefarious users of the dark web, or criminals who can cause us all kinds of harm using the data we shared. Herein lies the paradox of personalization and privacy. To solve this paradox, we acknowledge that there has to be a trade-off between data privacy and personalization, and an optimized match on trust, value etc. between the data owners (data subjects) and data buyers. In this paper, we propose approaches to handle these trade-offs in an optimized way with the acceptance threshold determined by a methodology to optimize the match between trust and value perceptions specific to an individual, and in context of the trust we place on the data controller, processor or buyer entity with which we share and entrust our data for the exchange of value provided.