Abstract

Digital platforms increasingly rely on user data to deliver personalized services, exposing users to the risk of privacy loss. Privacy-enhancing technologies (PETs) can mitigate these risks, but their adoption is costly and platforms may have incentives that differ from those of a welfare-maximizing policymaker. This work-in-progress studies a setting in which a platform endogenously chooses both the quality of personalization and its investment in PETs. Users are heterogeneous in their taste of personalization and in their sensitivity to privacy loss, and they decide whether to share data to access personalized services. We compare the platform’s privately optimal PETs investment to the socially optimal level that would maximize welfare. The analysis is designed to identify conditions under which market-driven privacy protection leads to underinvestment or overinvestment related to the social optimum, and to assess how policy instruments such as subsidies, fines, or restitution schemes can realign incentives. As a work in progress, we lay out the model structure, key assumptions, and testable conjectures that will guide the formal analysis.

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