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Advertising

Advertising PET decisions usually involve measurement under identifier loss, platform governance, small-segment leakage, and incentives that are not always aligned.

Scenario Playbook

Scenario Primary PET Supporting PETs Why What can go wrong What to measure
Advertiser measures campaign performance in a platform Clean room DP thresholds, query review, audit logs Platform can enforce controlled queries and output rules Platform sees too much, segments are tiny, query rules are gamed Minimum cohort size, query history, measurement lift
Two parties estimate audience overlap PSI Clean-room controls, DP counts Nonmatching users need not be revealed Repeated matching learns user lists Match precision, repeat-query limits, output granularity
Attribution without user-level tracking Attribution API / private aggregation DP-style noise, delay, thresholds Measurement survives without raw user-level identifiers Utility drops, debugging becomes hard, incentives shift Attribution error, delay, conversion-value loss
Retail media collaboration Clean room or federated analytics DP, purpose controls Retailers and brands need joint measurement Output policy follows platform incentives, not user expectations Segment sizes, allowed use, advertiser workflow fit

Use This When

  • The output is aggregate measurement, attribution, or overlap.
  • Small segments can be thresholded, grouped, or noised.
  • Query history and repeated joins are controlled.
  • Platform governance is explicit and reviewable.

Avoid This When

  • The proposal recreates user-level tracking through repeated queries.
  • "Clean room" is used as a brand label without technical output controls.
  • The audience segment is too small to protect.
  • Consent, purpose limitation, or platform power is the real issue.

For platform measurement, start with a clean room + output thresholds. For list overlap, start with PSI + strict output limits. Add DP or private aggregation when repeated aggregate releases need stronger protection.

Failure Modes

  • Small segments reveal sensitive behavior.
  • Identity normalization leaks more than the analysis requires.
  • Repeated queries reconstruct user-level signals.
  • Noise and delay make optimization unstable.
  • Platform rules are opaque to advertisers or users.

Evaluation Checklist

  • What is the smallest segment that can be queried or reported?
  • Can users or devices be tracked across repeated analyses?
  • Is the platform operator inside the trust model?
  • What measurement utility is lost from noise, delay, or thresholds?
  • Are allowed uses and retention limits enforced?