Privacy & Surveillance in 2025

Surveillance technologies and data-centric services now permeate nearly every layer of the application stack. While many such systems enable productivity and safety, they also create avenues for abuse and systemic risk.

This post summarizes pragmatic approaches to architect systems that preserve user privacy while remaining operationally effective.

Threats

• Mass collection and centralized telemetry increase exposure when a single breach occurs.
• On-device analysis can be subverted if the device or firmware is compromised.
• Legal or jurisdictional pressures can force service providers to change safeguards overnight.

Design patterns for privacy-preserving systems

• Minimal data collection: collect only what you need; prefer ephemeral or aggregated telemetry.
• Client-side processing with verifiable attestations: whenever possible, run sensitive transforms client-side and attest results to servers using privacy-preserving proofs.
• Differential privacy and aggregation: for analytics, aggregate and add calibrated noise to limit identifiability.

Operational recommendations

1. Keep a documented data minimization policy and enforce it with automated checks.
2. Use encryption-in-transit everywhere and encrypt at rest with per-tenant keys where applicable.
3. Maintain clear legal & compliance playbooks for cross-border data requests and notify users when permissible.
4. Run regular privacy threat models and tabletop exercises for new features.

Users and engineers can both contribute to better privacy outcomes: engineers by designing systems that default to minimal exposure, and users by preferring services that provide transparency and verifiable guarantees.