A first-party data reality check

Part I in Anonym's Rewiring the Rules Series

First-party data - the kind users share willingly with brands they trust - is a marketer's most valuable and precious asset. But often, when it comes time to activate that data in the form of an advertising campaign, marketers are forced into trade-offs that create real risks. To run campaigns, many hand over this data to platforms or third parties under data collaboration terms that introduce a number of problems:

1. They give up exclusive control, undermining their competitive advantage and allowing others to benefit from relationships they earned.
2. They risk eroding customer trust, by sharing data in ways users never expected.
3. They avoid sharing - and thus activating - the data altogether, because it's too sensitive or risky to do so under current controls.

These scenarios are common not because marketers don't value or care to protect their data, but because until now there hasn't been a solution that defends the brands' data control interests as strongly as it serves the platform's.

A paradox at the heart of modern marketing

Fact: Advertising literally funds the internet. Ads, and much of the data behind them, fuel the commercial content we all enjoy online. Today's marketers are expected to drive extraordinary results with more data risks than ever, from fears of data leakage to compliance missteps, or overexposure to black-box platforms. That's a difficult balance to strike.

And while data fuels the internet - it is also a finite, high-value, and often sensitive, high-risk resource. Treating people's personal data with care isn't just ethical - it's business.

Most digital advertising workflows today involve advertisers sharing first-party data with platforms, typically through pixels or conversions APIs, to enable platform-side targeting, optimization, and measurement. It's a familiar model: more data in, better performance out.

But this approach can introduce meaningful trade-offs. Once data leaves a brand's environment, it becomes harder to maintain transparency, exclusivity, or long-term control. And for especially sensitive or strategic datasets, sharing may not be viable at all.

These challenges are only intensifying as generative AI accelerates demand for richer, more granular inputs, often with little regard for how that data is governed or protected.

What marketers need now are better ways to activate their data without crossing lines that compromise either performance or control. That's what privacy-first design makes possible. By flipping the traditional model, advertisers keep data within strict boundaries, retain ownership, and grant platforms access to only what's needed to deliver results - nothing more.

For platforms, this may feel like a constraint. But for the broader ecosystem, it's a much-needed reset-one that shows performance and protection no longer have to be at odds.

The challenge is, most of today's systems weren't built for that.

A quick primer on data collaboration options

We'll dig deeper here in a future post, but let's quickly address some of the tools - specifically data clean rooms (DCRs) and platform APIs-often positioned as an industry fix.

• Data Clean Rooms (DCRs) generally come in two flavors: platform-owned (like Google's Ads Data Hub or Meta's Advanced Analytics) and third-party solutions. Platform-owned DCRs are typically free to access but come with a heavy technical lift-they require significant engineering resources, custom integration, and advertisers must share customer-level data for matching in order to unlock performance insights.
• Additionally, third-party DCRs, while platform-agnostic, can be both costly and complex to implement. It's not uncommon for onboarding to take 6+ months and require specialized technical support. In both cases, the operational burden can be high, and the trade-offs in data control are often underestimated.
• Direct sharing methods, such as platform APIs, give access, but not control. Once your data enters a platform API, it's absorbed into their black box and you can't audit the process or output. Direct sharing methods can expose user-level data to the platform with no privacy guarantees. If you need flexibility or custom collaboration, you're stuck. APIs are rigid by design.
• And then there are Privacy-Enhancing Technologies (PETs), which we'll cover more thoroughly in Part 2 of this series. PETs are often positioned as a silver bullet for data collaboration's privacy challenges-and in many ways, they represent a meaningful leap forward.

But not all PETs or PET configurations are created equal. As the term gains traction, it's increasingly used as a catchall for tools that claim to protect data-but in practice, may still expose sensitive information or centralize control in ways that benefit the platform more than the advertiser. In some cases, PETs offer the appearance of security while reinforcing the same power imbalances they're meant to solve.

A new model: Privacy-Enhancing Technologies (PETs), purpose-built

We're proposing a different way forward: privacy-enhancing solutions purpose-built on confidential computing, secure computation, and differential privacy to help advertisers use data securely, independently, and with confidence drive success. A solution where raw data is not exposed or seen by outside or other' parties, not even during analysis or collaboration.

We've taken this into account, and designed a PET solution specifically for marketers. It's not a black box. It's not compliance theater. It's a new model that enables collaboration without exposure.

• No cross-party user identity sharing required between advertisers and platforms
• You control what's analyzed, what's shared, and what's learned.
• You don't need to trust the platform; you trust the gold standard PETs we've implemented.
• You can use your data to grow without worrying about it enriching anyone else, including your competitors.

The premise is novel: a PET design dedicated to real advertising use cases-audience creation, measurement, optimization - not academic experiments or compliance checklists. Marketers can use their most valuable data without handing it over, losing control, or compromising performance. They can define what's analyzed, what leaves, and what value is captured, but without the burden of overly complicated technical integrations.

In Part 2 of this series, we'll unpack more of what this purpose-built promise looks like, along with the differences between performative privacy and real protection, and what to look for when evaluating whether a solution actually puts your interests first.

Rethinking what advertisers should expect from data

Anonym was built to flip the script on the myth that protection and performance cancel each out, and to prove that privacy-first marketing isn't the constraint - it's the unlock. That data protection can literally power smarter, more strategic outcomes, and has the potential to build more trusted relationships between platforms and customers. Done well, a privacy-first architecture actually enables more untapped performance, and is the infrastructure we believe the next era of marketing should be built on.

This is an important shift. For years, data protection and performance have been treated as tradeoffs. But when the right systems are in place, data safeguarding through confidentiality and control can actually be a way to harness more of your insights-not less-while simultaneously respecting the people behind it.

We'll explore the mechanics in a future post, but for now make no mistake: the link between protection and performance isn't hypothetical - it's proven. And it's already underway.

Authored by Graham Mudd, SVP of Product at Mozilla's Anonym and Anonym Co-Founder

Coming up in the series

In Part 2 or our Rewiring the Rules series, we'll take a closer look at what privacy-enhancing technologies promise, how they actually work, and Anonym's innovative approach to helping marketers turn data control and confidentiality into a performance advantage.

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