Security, Cryptography, and Trusted Data Infrastructures for the Machine Service Economy

Research Vision:

A growing class of autonomous systems — vehicles, sensors, drones, robots, and AI agents—is increasingly providing services, consuming services, and settling transactions with one another. These Machine Services introduce new requirements for security, identity, and data architectures: Who is the acting entity? Can the transmitted data be trusted? Is one machine authorized to act on behalf of another?

My research focuses on the cryptographic, protocol, and systems foundations required to make identity, provenance, and integrity verifiable across machine-driven value chains — at a scale and speed where human verification is no longer feasible.

1. Cryptographic Machine and Agent Identity — Device-bound keys, hardware roots of trust (TPMs, Secure Elements), certificate lifecycle management, and identity federation across edge, cloud, and on-chain systems.

2. Trusted Data Infrastructures — Signed and chained data streams, tamper-resistant telemetry, and audit trails for AI, compliance, and automated settlement.

3. Secure Machine-to-Machine and Agent-to-Agent Communication — End-to-end encrypted peer-to-peer protocols, mutual authentication, and scalable networking for large machine fleets.

4. Architectures for Autonomous Service Economies — Contract, settlement, and incentive mechanisms for Machine Services at the intersection of identity, decentralized settlement, and real-world service delivery.

5. Security of Agentic AI Systems — Hardening LLM- and MCP-based architectures against prompt injection, tool poisoning, and attacks in multi-agent environments.

6. Sovereign and Privacy-Preserving System Architectures — Privacy-by-design, data residency, regulatory auditability, and decentralized architectures for critical infrastructure and industrial IoT systems.

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• Fachbereich 1: Ingenieurwissenschaften - Energie und Information
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