Privacy-First ML & Experimentation: Designing Systems Without User IDs

Persistent user and device identifiers have powered large-scale machine learning and analytics but introduce major privacy risks. These include cross-session linkability, re-identification, and regulatory exposure under GDPR and other emerging data protection laws. Privacy-enhancing technologies now show that high-utility analytics and machine learning are possible without centralizing raw user data or relying on stable identifiers. This work presents a privacy‑by‑design architecture that eliminates persistent identifiers through several key methods: attribute generalization and cohort hashing to reduce fingerprinting risk, on‑device attribution with k‑anonymized experimentation enhanced by optional differential privacy noise, federated learning with calibrated privacy budgets and secure aggregation, and strict time‑to‑live retention policies combined with schema pruning to minimize long-term data exposure. Simulation results demonstrate that these techniques remove fingerprint singleton ratios while preserving unbiased effect estimates, maintain high model utility despite decentralized computation, and reduce stored data volumes significantly. Together, these approaches allow organizations to deliver personalization and experimentation capabilities while meeting regulatory requirements, adapting to evolving platform privacy controls, and building long-term user trust through transparent privacy practices.