license: cc-by-nc-sa-4.0
tags:
- web-privacy
- tracker-detection
- entity-attribution
- feedforward
- safetensors
- webassembly
datasets:
- olafuraron/tracker-radar-ml
Tracking Entity Classifier
Predicts which company owns a third-party tracking domain based on behavioral patterns from DuckDuckGo's Tracker Radar dataset. No ownership metadata is used as input — the model learns to identify entities from API usage, cookie behavior, resource types, and prevalence patterns.
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Labels
13 tracking-related entities:
Adobe Inc., ByteDance Ltd., Comcast Corporation, Conversant LLC, Google LLC, HubSpot Inc., Impact, Leven Labs Inc. DBA Admiral, Microsoft Corporation, Oracle Corporation, Salesforce.com Inc., Yahoo Inc., Yandex LLC
Performance
- Accuracy: 58.5%
- Weighted F1: 0.604
- Training data: 731 domains from Tracker Radar US region
- Features: 164 behavioral features
Strong per-entity results for distinctive entities: Leven Labs (F1 0.93), Google (F1 0.75), Microsoft (F1 0.65). Less reliable for smaller entities with few training samples.
Architecture
Feedforward neural network: 164 → 128 → 64 → 13 with ReLU activations and dropout (0.2). Model size: 118.5 KB.
Designed for on-device inference via Kjarni WebAssembly runtime with SIMD128 acceleration.
Usage
Features must be standardized using the provided scaler (mean and scale in tracking_entity_classifier_scaler.json) before inference. This model is most meaningful when applied to domains already identified as ad tech by the entity cluster classifier.
Context
This model demonstrates that tracking companies have identifiable behavioral fingerprints — their domains exhibit characteristic patterns of API usage, cookie behavior, and web presence that distinguish them from other entities. See TrackerML for the full project.
Links
License
CC-BY-NC-SA 4.0 (derived from DuckDuckGo Tracker Radar).