2. Aggregated signals & Privacy-preserving matching

We are researching how accurate service matching and personalization can be achieved without storing detailed individual user profiles or long-term behavioral histories.

Instead of relying on raw interaction logs or surveillance-based profiling, this research explores how aggregated, derived signals can be used to represent intent, preferences and demand patterns in a privacy-preserving way. The objective is to understand how high-quality matching between users and service providers can be delivered while minimizing retained data, computational overhead and privacy risk.

Key themes:

• From raw interactions to aggregated signals
  How ephemeral interaction data can be transformed into derived, non-invertible signals early in the data pipeline.

• Utility vs privacy trade-offs
  Studying how much data is actually required to achieve accurate matching, and where diminishing returns begin.

• Signal-based recommendation models
  Exploring how recommendations can be generated from aggregated intent signals rather than individual behavioral profiling.

• Stability over time without detailed histories
  Researching how long-term tendencies can be represented through summarized preference structures instead of persistent raw data storage.

• Scalable and energy-efficient AI
  Evaluating how aggregated signal architectures reduce repeated data processing and AI inference, contributing to more efficient digital infrastructure.