Data Privacy in Digital Markets

Projektinformationen

PRIVDIMA

ID Finanzhilfevereinbarung: 101096682

DOI

10.3030/101096682

EK-Unterschriftsdatum 22 Mai 2023

Startdatum 1 Oktober 2023

Enddatum 30 September 2028

Finanziert unter

• European Research Council (ERC)

Gesamtkosten

€ 1 860 125,00

EU-Beitrag

€ 1 860 125,00

Koordiniert durch

HUMBOLDT-UNIVERSITAET ZU BERLIN
Germany

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

Berichtszeitraum: 2023-10-01 bis 2026-03-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

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.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

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.