Privacy Trading in the Apps and IoT Age: Markets and Computation

In the era of the mobile apps and IoT, 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 fragmented and inefficient. On one hand, end-users are not able to control access (either technologically, by policy, or psychologically) to their personal data (currently only possible for GDPR regulated countries) 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 apps and ad-driven entities (e.g., an ad-network) at a cost of trust and regulatory accountability. In such a context, data holders (e.g., apps) may take advantage of the individuals’ inability to fully comprehend and anticipate the potential uses of their private information with detrimental effects for aggregate social welfare (e.g., Facebook-Cambridge Analytica, and Gmail case studies). In this talk, we investigate the problem of the existence and computationally efficient engineering design of efficient market ecosystems (and their subsequent implications to law and policy) that aim to achieve a maximum social welfare state among competing data holders by preserving the heterogeneous privacy preservation constraints up to certain compromise levels, induced by their clients, and at the same time satisfying requirements of agencies (e.g., advertising organizations) that collect and trade client data for the purpose of targeted advertising, assuming the potential practical inevitability of some amount inappropriate data leakage on behalf of the data holders.