Data Privacy in Digital Markets

Project Information

PRIVDIMA

Grant agreement ID: 101096682

DOI

10.3030/101096682

EC signature date 22 May 2023

Start date 1 October 2023

End date 30 September 2028

Funded under

• European Research Council (ERC)

Total cost

€ 1 860 125,00

EU contribution

€ 1 860 125,00

Coordinated by

HUMBOLDT-UNIVERSITAET ZU BERLIN
Germany

Periodic Reporting for period 1 - PRIVDIMA (Data Privacy in Digital Markets)

Reporting period: 2023-10-01 to 2026-03-31

Summary of the context and overall objectives of the project

In the ever-evolving landscape of digital markets, the project PRIVDIMA takes center stage, spotlighting the often overlooked verifiability effect of consumer data and its profound implications for the regulation of data use and collection. At its core, the verifiability effect signifies the transformation of consumer data into a tangible, verifiable asset, presenting a paradigm shift in our understanding of privacy dynamics.

Traditional economic theories have predominantly focused on the regulation of private information that remains non-verifiable. However, PRIVDIMA recognizes the pivotal role of verifiability, challenging the prevailing oversight in existing frameworks. The project is driven by the conviction that overlooking the verifiability effect leads to misguided regulations, hindering the effective governance of digital markets.

Objectives:
1. Advancing Economic Theory:
• a. Mechanism Design
• b. Information Design
2. Policy Implications for Data Use:
Delving beyond theoretical advancements, PRIVDIMA aims to derive practical policy implications for data privacy in the context of data use. By weaving verifiability into the fabric of these policies, the project seeks to ensure a fair distribution of benefits from digitalization. This not only promotes equity and fairness but also establishes a crucial link between regulatory frameworks and public trust.
3. Policy Implications for Data Collection:
Extending its reach, PRIVDIMA addresses the critical aspect of data collection. By formulating policy implications that account for the verifiability effect, the project strives to strike a delicate balance—facilitating innovation and data-driven advancements while safeguarding individual privacy rights.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Objective 1: Extending Theories of Design by Verifiability. We achieved a major theoretical breakthrough by successfully extending mechanism design theory to handle general verifiability structures. The key innovation involves using extended utility functions that ensure all player types have identical strategy spaces - a requirement for applying standard mechanism design tools that was previously violated when agents could present different types of evidence.

Objective 2: Data Use Regulation. We characterized optimal data privacy rules for both long- term and short-term contracting scenarios. For long-term contracts, our analysis demonstrates that optimal regulation focuses on preventing full unraveling through partial certifiability mechanisms. For short-term contracts, we identified how ratchet effects fundamentally alter optimal privacy rules, requiring stronger data collection restrictions.

Objective 3: Data Collection Regulation. We developed comprehensive characterizations of verification structures that prevent unraveling most effectively. Our analysis identifies optimal data collection policies using tools of partial certifiability, establishing that buyer-optimal verifiability structures systematically prevent full information revelation while maintaining beneficial aspects of data collection.ability structures systematically prevent full information revelation while maintaining beneficial aspects of data collection.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Breakthrough 1: Resolution of the Revelation Principle Problem
We demonstrate that the intractability stems from improper game- theoretic foundations rather than inherent complexity. By rigorously applying Harsanyi's (1967) construction through evidence-extended utility functions, we transform any verifiability structure into a standard Bayesian game where classical revelation principles apply. This breakthrough, documented in "Principled Mechanism Design with Evidence" (Schweighofer-Kodritsch, Strausz) and validated in "Unidirectional Incentive Compatibility" (Krähmer, Strausz, JET forthcoming), enables analysis of previously intractable problems across all verifiability structures.

Breakthrough 2: Partial Certifiability as Optimal Regulatory Tool
We discovered that strategically designed partial certifiability mechanisms can achieve first-best consumer outcomes in digital markets. Our characterization of buyer-optimal verifiability structures shows precisely how to limit information revelation to prevent consumer harm while preserving market efficiency. This counterintuitive insight that "less information can be better" provides concrete regulatory tools, demonstrating that partial data collection often dominates both full disclosure and complete privacy. This breakthrough transforms partial certifiability from a theoretical curiosity into a practical regulatory instrument.

Breakthrough 3: Ratchet Effects Fundamentally Transform Privacy Regulation
Our work "Oligopolistic Information Markets" (Achim, Strausz) develops the first comprehensive theory of oligopolistic verification markets, revealing fundamental departures from standard oligopoly theory. We show that information markets exhibit unique features: verification creates positive externalities across competitors; market concentration can sometimes improve consumer welfare by enabling better verification standards; and traditional competition policy tools may backfire when applied without accounting for information dynamics. This breakthrough provides competition authorities with appropriate tools for analyzing mergers and market power in data-driven industries.