{"id":1746,"date":"2026-07-14T13:00:00","date_gmt":"2026-07-14T18:00:00","guid":{"rendered":"https:\/\/tolinku.com\/blog\/?p=1746"},"modified":"2026-03-07T03:50:06","modified_gmt":"2026-03-07T08:50:06","slug":"attribution-data-clean-rooms","status":"publish","type":"post","link":"https:\/\/tolinku.com\/blog\/attribution-data-clean-rooms\/","title":{"rendered":"Attribution Data Clean Rooms: Privacy-Safe Measurement"},"content":{"rendered":"\n

Data clean rooms are secure environments where two parties (typically an advertiser and a publisher or ad network) can match their first-party data without either party seeing the other's raw data. In mobile attribution, clean rooms let you measure campaign performance without sharing user-level data with ad networks, and without ad networks sharing their user data with you.<\/p>\n\n\n\n

This guide explains how data clean rooms work for mobile attribution. For attribution and privacy regulations, see mobile attribution under GDPR<\/a>. For SKAdNetwork, see SKAdNetwork and SKAN 5.0: complete developer guide<\/a>.<\/p>\n\n\n\n

Why Data Clean Rooms Exist<\/h2>\n\n\n\n

Traditional mobile attribution works by sharing user-level data between parties:<\/p>\n\n\n\n

    \n
  1. Ad network shares click data (device ID, timestamp, campaign).<\/li>\n
  2. Attribution provider matches clicks to installs using device IDs.<\/li>\n
  3. Results are shared back to the ad network.<\/li>\n<\/ol>\n\n\n\n

    This model requires both parties to share raw user data, which conflicts with privacy regulations (GDPR<\/a>, CCPA<\/a>) and platform restrictions (ATT, Privacy Sandbox).<\/p>\n\n\n\n

    Data clean rooms solve this by providing a neutral environment where matching happens without data exposure.<\/p>\n\n\n\n

    How Clean Rooms Work<\/h2>\n\n\n\n

    The Basic Flow<\/h3>\n\n\n\n
    1. Advertiser uploads first-party data (user IDs, conversion events)\n   \u2192 Data is encrypted\/hashed before upload\n\n2. Publisher\/Ad Network uploads campaign data (impressions, clicks, user IDs)\n   \u2192 Data is encrypted\/hashed before upload\n\n3. Clean room matches records using hashed identifiers\n   \u2192 Neither party sees the other's raw data\n\n4. Clean room outputs aggregate results\n   \u2192 "Campaign X drove 1,200 installs with $45,000 revenue"\n   \u2192 No user-level data is exposed\n<\/code><\/pre>\n\n\n\n
    Privacy Protections<\/h3>\n\n\n\n
    Clean rooms enforce privacy through several mechanisms:<\/p>\n\n\n\n
    \n\n\n\n\n\n\n\n\n
    Protection<\/th>\nHow It Works<\/th>\n<\/tr>\n<\/thead>\n
    Data isolation<\/td>\nEach party's data is stored separately and encrypted<\/td>\n<\/tr>\n
    No raw data export<\/td>\nNeither party can export the other's raw data<\/td>\n<\/tr>\n
    Aggregate-only output<\/td>\nResults are aggregated (no individual user records)<\/td>\n<\/tr>\n
    Minimum thresholds<\/td>\nResults with fewer than a minimum number of users (e.g., 50) are suppressed<\/td>\n<\/tr>\n
    Query restrictions<\/td>\nOnly pre-approved queries are allowed (no arbitrary SQL)<\/td>\n<\/tr>\n
    Audit logging<\/td>\nAll queries and access are logged for compliance<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
    Clean Room Providers<\/h2>\n\n\n\n
    Major Platforms<\/h3>\n\n\n\n
    \n\n\n\n\n\n\n\n\n
    Provider<\/th>\nType<\/th>\nStrengths<\/th>\n<\/tr>\n<\/thead>\n
    Google Ads Data Hub<\/a><\/td>\nWalled garden<\/td>\nAccess to Google's ad data (Search, YouTube, Display)<\/td>\n<\/tr>\n
    Meta Advanced Analytics<\/a><\/td>\nWalled garden<\/td>\nAccess to Meta's ad data (Facebook, Instagram)<\/td>\n<\/tr>\n
    AWS Clean Rooms<\/a><\/td>\nInfrastructure<\/td>\nFlexible, use with any data partner<\/td>\n<\/tr>\n
    Snowflake Data Clean Rooms<\/a><\/td>\nInfrastructure<\/td>\nSQL-based, integrates with existing Snowflake data<\/td>\n<\/tr>\n
    InfoSum<\/a><\/td>\nIndependent<\/td>\nDecentralized (data never leaves your infrastructure)<\/td>\n<\/tr>\n
    LiveRamp Data Collaboration<\/a><\/td>\nIndependent<\/td>\nIdentity resolution + clean room<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
    Walled Garden vs. Infrastructure Clean Rooms<\/h3>\n\n\n\n
    Walled garden clean rooms<\/strong> (Google, Meta) are built by the ad platform itself. You bring your conversion data; they have the ad exposure data. You can only measure campaigns on that specific platform.<\/p>\n\n\n\n
    Infrastructure clean rooms<\/strong> (AWS, Snowflake) provide the technology, and you choose your data partners. More flexible but require more setup.<\/p>\n\n\n\n
    Clean Rooms for Mobile Attribution<\/h2>\n\n\n\n
    Measuring Install Campaigns<\/h3>\n\n\n\n
    Instead of sharing device IDs with an ad network for real-time attribution, you can use a clean room for aggregate measurement:<\/p>\n\n\n\n
    -- Example clean room query (aggregate results only)\nSELECT\n  campaign_id,\n  ad_group_id,\n  COUNT(DISTINCT matched_users) AS attributed_installs,\n  SUM(revenue_30d) AS revenue,\n  SUM(revenue_30d) \/ COUNT(DISTINCT matched_users) AS revenue_per_install\nFROM\n  advertiser_installs\nJOIN\n  publisher_impressions\nON\n  hashed_user_id = hashed_user_id\nWHERE\n  impression_time < install_time\n  AND install_time - impression_time < INTERVAL '7 days'\nGROUP BY\n  campaign_id, ad_group_id\nHAVING\n  COUNT(DISTINCT matched_users) >= 50  -- minimum threshold\n<\/code><\/pre>\n\n\n\n
    Comparing to Traditional Attribution<\/h3>\n\n\n\n
    \n\n\n\n\n\n\n\n\n\n
    Aspect<\/th>\nTraditional Attribution<\/th>\nClean Room Attribution<\/th>\n<\/tr>\n<\/thead>\n
    Data sharing<\/td>\nUser-level data shared between parties<\/td>\nNo raw data shared<\/td>\n<\/tr>\n
    Granularity<\/td>\nUser-level (real-time)<\/td>\nAggregate (batch)<\/td>\n<\/tr>\n
    Latency<\/td>\nReal-time (seconds to minutes)<\/td>\nBatch (hours to days)<\/td>\n<\/tr>\n
    Campaign optimization<\/td>\nReal-time bidding optimization<\/td>\nPost-campaign analysis<\/td>\n<\/tr>\n
    Privacy compliance<\/td>\nRequires consent for data sharing<\/td>\nPrivacy-safe by design<\/td>\n<\/tr>\n
    Cost<\/td>\nIncluded in attribution provider fee<\/td>\nSeparate cost (query fees, infrastructure)<\/td>\n<\/tr>\n
    Complexity<\/td>\nLow (SDK integration)<\/td>\nHigh (data engineering required)<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
    When Clean Rooms Make Sense<\/h2>\n\n\n\n
    Good Use Cases<\/h3>\n\n\n\n