About

Synheart Behavior is a privacy-preserving framework that models human digital behavior through interaction dynamics rather than content. It transforms low-level events such as taps, scrolling, idle time, and app switching into structured, numerical, and interpretable behavioral representations across event, session, and daily timescales. By focusing on timing, intensity, fragmentation, and interruptions without collecting semantic data or personal identifiers, the framework enables reliable behavioral analysis while maintaining strong privacy guarantees.

What is SynHeart Behavior?

In this paper, Synheart introduces a privacy-preserving framework for modeling digital behavior, defining digital behavior as:

The measurable temporal and structural patterns of a user's interaction with digital systems, independent of content, semantics, or intent.

Numerical Representation of Synheart Behavior

The Synheart behavior differs fundamentally from traditional psychological or social behavior models. Instead of attempting to infer internal mental states, social relationships, or real-world actions, it focuses exclusively on observable interaction dynamics, such as interaction frequency, session continuity, responsiveness to interruptions, and consistency of use over time. While cognitive and emotional states may indirectly influence these patterns, the framework does not rely on content, semantics, or inferred intentions, ensuring a privacy-preserving approach.

What We Built

• A privacy-first framework that converts raw interaction events into structured numerical behavior metrics
• Behavioral representations across event-level, session-level, and daily timescales
• Interpretable metrics capturing interaction timing, intensity, fragmentation, and interruptions
• Standardized behavioral signals usable directly for downstream modeling and analysis
• An SDK designed to operate without collecting content or personal identifiers

Our Vision

• Enable meaningful understanding of human digital behavior without compromising privacy
• Shift behavior analysis from content-based to interaction-based modeling
• Provide standardized, interpretable behavioral representations across devices and studies
• Bridge raw interaction data and practical, ethical behavioral insights
• Support behavior-aware systems, digital well-being analysis, and adaptive AI models

Scope and assumptions

The framework is restricted to digital interaction metadata, excluding social content, communication meaning, or real-world behavioral observations. Higher-level interpretations emerge solely from temporal and structural patterns of interactions, preserving both user privacy and analytical rigor.