Despite backing from Apple and Google, why does differential privacy adoption remain limited?

It needs large datasets, is mathematically and operationally hard to build, and many organizations don't see enough benefit to justify the engineering cost.

Three barriers:

• Requires large datasets — the injected noise makes DP unsuitable for small datasets, where noise overwhelms the signal and results become inaccurate. The bigger the dataset, the better the privacy/accuracy balance.
• Implementation complexity — building a system that accepts arbitrary queries, calibrates noise per query type, tracks cumulative privacy-budget consumption, and prevents leakage through query combinations is practically impossible for most organizations. It demands deep mathematical expertise.
• Cost vs. benefit — it's far simpler to collect raw data and apply traditional anonymization (even if weaker), so many companies don't see enough value to justify the investment and maintenance.

Even companies that have adopted DP typically apply it to only a small fraction of their data collection.

Tip: DP's hardest engineering problem is the privacy budget: every query spends it, and once it's gone, further queries leak. Managing that across an organization is the real bottleneck.