Privacy trading in the surveillance capitalism age viewpoints on 'privacy-preserving' societal value creation

Abstract

In the modern era of the mobile apps (part of the era of surveillance capitalism , a famously coined term by Shoshana Zuboff), huge quantities of data about individuals and their activities offer a wave of opportunities for economic and societal value creation. However, the current personal data ecosystem is mostly de-regulated, fragmented, and inefficient. On one hand, end-users are often not able to control access (either technologically, by policy, or psychologically) to their personal data which results in issues related to privacy, personal data ownership, transparency, and value distribution. On the other hand, this puts the burden of managing and protecting user data on profit-driven apps and ad-driven entities (e.g., an ad-network) at a cost of trust and regulatory accountability. Data holders (e.g., apps) may hence take commercial advantage of the individuals' inability to fully anticipate the potential uses of their private information, with detrimental effects for social welfare. As steps to improve social welfare, we comment on the the existence and design of efficient consumer-data releasing ecosystems aimed at achieving a maximum social welfare state amongst competing data holders. In view of (a) the behavioral assumption that humans are 'compromising' beings, (b) privacy not being a well-boundaried good, and (c) the practical inevitability of inappropriate data leakage by data holders upstream in the supply-chain, we showcase the idea of a regulated and radical privacy trading mechanism that preserves the heterogeneous privacy preservation constraints (at an aggregate consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfying commercial requirements of agencies (e.g., advertising organizations) that collect and trade client data for the purpose of behavioral advertising. More specifically, our idea merges supply function economics , introduced by Klemperer and Meyer, with differential privacy, that, together with their powerful theoretical properties, leads to a stable and efficient, i.e., a maximum social welfare, state, and that too in an algorithmically scalable manner. As part of future research, we also discuss interesting additional techno-economic challenges related to realizing effective privacy trading ecosystems.