Data as of Oct 7, 2026A topic in Mobile App Growth & Engagement Tools.
Reviewed by Dimitry Apollonsky
ChatGPT SearchOct 7, 2026
Yes. If your requirement is specifically privacy-centric attribution after Apple’s IDFA changes, I’d shortlist AppsFlyer, Adjust, and Singular, with Singular standing out if privacy-by-design is the primary criterion.
| MMP | Post-IDFA / SKAN | Privacy posture | Best fit |
|---|---|---|---|
| Singular | Strong SKAdNetwork + AdAttributionKit support |
Which mobile measurement partners are best for privacy-focused attribution now that IDFA has changed?
Can you recommend an MMP that handles attribution in a privacy-first way now that IDFA is restricted?
| Explicitly avoids fingerprinting/device graphs for SKAN |
| Privacy-first measurement |
| AppsFlyer | Very mature SKAN/AAK + modeled attribution | Strong controls for IDFA/IDFV, IP masking, anonymization, post-install de-identification | Broad enterprise MMP |
| Adjust | Strong SKAN + evolving AdAttributionKit | Apple-native aggregated attribution without device-level SKAN data | Enterprise attribution + analytics |
| Branch | Strong SKAN + predictive aggregate measurement | Aggregation/anonymization emphasis | Deep linking + attribution |
1. Singular — strongest privacy-first candidate
Singular explicitly positions its SKAdNetwork product around not using fingerprinting or device graphs, while still providing modeled measurement to fill gaps in Apple's privacy-preserving data.
It supports SKAN alongside traditional attribution, and its documentation confirms support for Apple's AdAttributionKit (AAK) as well.
A particularly useful feature is that you can send SKAN/AAK postback copies directly to Singular and export the data to your own warehouse.
2. AppsFlyer — strongest all-around enterprise choice
AppsFlyer has a very mature privacy architecture. It supports SKAN without IDFA/ATT, probabilistic modeling that is explicitly designed to produce aggregate campaign-level results rather than identify individual devices, and multiple privacy controls.
Its privacy controls include device-ID opt-out, IP masking, anonymization, data-sharing controls, and post-install de-identification.
It also supports direct SKAN/AdAttributionKit postback copies, which is valuable if you want an independent measurement layer rather than relying solely on ad-network reporting.
3. Adjust — strong traditional MMP with privacy-native Apple measurement
Adjust's SKAN implementation is genuinely privacy-centric: Apple performs the attribution, and no device-level data is recorded or shared through SKAN. Adjust aggregates the resulting postbacks from networks.
Adjust is also actively tracking AdAttributionKit developments, including newer re-engagement and attribution capabilities.
"Post-IDFA attribution" can mean two rather different things:
For example, AppsFlyer says its probabilistic modeling does not use unique IDs and produces aggregate campaign reporting, while Singular explicitly says its SKAN solution avoids fingerprinting and device graphs.
If you tell me your use case (e.g. consumer app, gaming, fintech, subscription app, or ad-tech platform), I can narrow this to 2–3 MMPs and compare pricing, privacy architecture, SKAN/AAK capabilities, incrementality, data clean rooms, and integrations with Meta/Google/TikTok.
Post-IDFA (Identifier for Advertisers) changes and the widespread adoption of Apple's App Tracking Transparency (ATT), the mobile measurement partner (MMP) landscape has fully shifted toward privacy-centric, aggregated, and modeled attribution frameworks.
The top tier MMPs that dominate the market all provide robust, privacy-safe attribution suites utilizing Apple's SKAdNetwork (SKAN), AdAttributionKit , and advanced privacy-preserving data clean rooms or probabilistic modeling.
Leading mobile measurement partners specializing in privacy-centric attribution include:
To help narrow down the best fit, could you share:
