On September 12, 2024, Google announced a new privacy-focused feature called Confidential Matching aimed at advertisers using their Customer Match and Enhanced Conversions tools within the Google Marketing Platform (GMP). This announcement comes amid the ever-rising concerns over privacy regulations in the EU (e.g., GDPR) and also increasingly in the US (e.g., CCPA), which push advertisers to handle customer data more securely. But is it truly a breakthrough, or does it merely obscure underlying concerns about data sharing?
In this post, I’ll explain how Confidential Matching works, its role in Google’s marketing ecosystem, and critically assess whether it provides a real solution for privacy-focused advertisers. By the end, you’ll have a clearer understanding of whether this new feature is a significant step forward or “just” another method to maintain data control under the guise of privacy protection.
What is Confidential Matching?
As privacy regulations like GDPR and CCPA are enforced more and more, advertisers face increasing challenges when using customer data for ad targeting and measurement. Traditionally, tools like Customer Match and Enhanced Conversions required advertisers to share sensitive personal data, such as email addresses or phone numbers, with Google. Confidential Matching offers a new solution by allowing advertisers to match customer data with Google’s records without directly sharing that raw information.
This is achieved using a technology called Trusted Execution Environments (TEEs), which act as secure “digital vaults”. Think of a TEE as a locked, isolated chamber where data can be processed without external access. According to Google, TEEs ensure that customer data remains secure while still allowing advertisers to gain insights and maintain advertising effectiveness.
In short, Confidential Matching allows advertisers to benefit from matching their data with Google’s datasets without directly sharing private information—aiming to balance privacy with performance.
What are Customer Match and Enhanced Conversions?
Customer Match and Enhanced Conversions are two powerful tools in Google’s advertising suite, both relying on customer data to improve targeting and measurement. Confidential Matching is set to enhance these tools by allowing advertisers to match customer data with Google’s user base more securely, thanks to TEEs.
But before we have a closer look at Confidential Matching, let’s first uncover what each of these tools does and why customer data is so crucial to their effectiveness.
Customer Match - Target users that you know
For effective targeted advertising, it is essential to identify subsets of users who are relevant to the business in one way or another. These users might be likely to buy soon, about to churn shortly, or about to exhibit another form of behavior. Businesses often use various data sources to create these segments and identify relevant subsets of users.
One of the most valuable data sources is the customer data businesses have collected over time. This data can include email addresses, phone numbers, and behavioral data (e.g., purchase history, website visits and actions). Once advertisers have collected this data and identified relevant subsets of their customers, they can use Customer Match to upload the users’ contact information to Google. The upload can be done using manual CSV uploads, the respective APIs, or the Google Ads Data Manager (direct connection to various cloud data storage systems). Google also offers the option to encrypt the data using the SHA256 algorithm before uploading it.
It is important to note that ever since Google started enforcing its EU consent policy, advertisers need to ensure that they also pass the required consent signals.
Customer Match - Basic Process
Google then compares the uploaded customer data with its user database to find matches and creates a so-called “Customer Match audience” from all the matched entries. The data can also be used to create lookalike audiences, which are users who share characteristics similar to those of the uploaded customer list. The resulting audiences can then be attached to campaigns and ads with messaging directly related to this specific subset of users, which obviously allows for very targeted messaging.
Users who are signed in to Google services (e.g., Gmail, YouTube), have given Google permission to use their data for this purpose, and match an entry in the uploaded customer list, which will then be exposed to the ads. This allows advertisers to target their existing customers with personalized ads and to reach new users who share similar characteristics with their existing customers.
A pretty powerful tool, don’t you agree? Unfortunately, there’s a catch. To use this feature, advertisers need to hand over (consented) contact data of their customers to Google and rely on Google not using the data for any (not consented) purposes.
Enhanced Conversions - Get better insights into your campaign performance
While Customer Match is a tool for delivering targeted advertising messaging, Enhanced Conversions is primarily concerned with enhancing the measurement of GMP campaign and ad performance. Both Enhanced Conversions and Customer Match share a common characteristic, though: They rely on the advertiser sharing customer contact data with Google, which Google then matches against its own database entries.
Enhanced Conversions - Basic Process
In Enhanced Conversions, the customer data is used to more reliably attribute a user’s ad exposure or interaction with a conversion registered on the advertiser’s website. For Google signed-in users, Google can keep track of all ad impressions and clicks that a specific user had within their ad network. With the advertiser passing on the user’s contact details upon a conversion, Google can match its customer data again against the advertiser’s customer data.
Matching these deterministic identifiers (e.g., eail addresses) allows Google to provide the advertiser with accurate reporting on their campaign performance. Google doesn’t have to rely on unstable third-party cookies for any matched user. Still, it can use the advertiser’s customer data to link ad exposure to business goals. Additionally, better attribution of ad exposure will allow Google’s algorithms to optimize the campaigns better, eventually yielding better results for the advertiser.
As you can see, at the end of the day, the mechanism and the associated risks are similar to those of Customer Match. What differs is simply how the data is used within Google’s systems.
Why Confidential Matching?
With the erosion of third-party cookies and other tracking technologies, Google has pushed advertisers to adopt technologies that rely on them sharing more PII with Google. Especially the use of Enhanced Conversions and Customer Match has been promoted as the salvation for advertisers to continue measuring and targeting users across the web. Both technologies require the advertiser to share PII, such as their customers’ email addresses and phone numbers, with Google. Google then maps these data points against their user records (e.g., from Google Chrome logged-in users) to improve the measurability of campaign performance and enable retargeting use cases. While the results for businesses that adopted these methods have been nothing short of impressive, many businesses have been (rightfully) hesitant to share their customer data with Google due to privacy concerns.
Confidential Matching for Customer Match (and soon for Enhanced Conversions) aims to provide a secure way for advertisers to match their customer data with Google’s own data in a “privacy-preserving” manner using Trusted Execution Environments (TEEs). With the introduction of Confidential Matching, Google seeks to address the ever-growing concerns around data privacy and security in digital advertising about Personal Identifiable Information (PII) - in this case, customer data like email addresses, phone numbers, etc.. especially in light of privacy law requirements like the GDPR in Europe and similar laws in the United States, like the CCPA.
Before Confidential Matching, advertisers had to share all their customer data relevant to Customer Match or Enhanced Conversions with Google. Then, Google would use the data it could match within its downstream systems. For the (potentially large) share of unmatched user data, businesses had to trust Google to not further process that data for their own purposes. With Confidential Matching, which operates on TEEs, Google has found a way to match the data without ever seeing the original data itself.
What are Trusted Execution Environments (TEEs)?
A TEE—also referred to as “Confidential computing”—is an environment for executing code and processing data in a secure and isolated manner. Think of TEEs like a clean room where sensitive (or any other) data is processed in a way that the TEE operator (in this case, Google) cannot access it. One or multiple data owners send data to the system. The data is encrypted before it enters the TEE and finally decrypted within the TEE.
The TEE ensures that the data is processed securely and that the results are only accessible to authorized parties. Furthermore, the TEE can provide attestation, which is cryptographic proof that the code running in the TEE is genuine and has not been tampered with. At the same time, the TEE is auditable, meaning that the data owner (or a third party) can verify that the data has been processed according to the requirements.
Hence, the core characteristics of TEEs as implemented by Google can be summarized as follows:
- Data Isolation: Google, as the operator of the TEE, cannot access the data processed within the TEE.
- Attestation: The TEE provides cryptographic proof that the code running within it is genuine and has not been tampered with.
- Auditable: The data owner can verify that the data has been processed according to the requirements.
In general, TEEs are an exciting technology for enhancing data privacy and security, as they allow data owners to share their data without exposing each party’s actual data.
Bringing it all together: Confidential Matching
So, how does all of this fit together, and how does it “enhance privacy” (as Google claims)?
There are two main problems with Customer Match and Enhanced Conversions. The first one is that, in most cases, advertisers share too much data with Google. They’ll pass on any customer data and hope that Google finds plenty of matches in their database to enhance their targeting and measurement. The second problem is that once advertisers share the data, they have no control over how Google processes and uses it. Google might use the valuable customer data for their own purposes (other than retargeting or measurement enhancements), for which the advertiser didn’t obtain valid user consent.
Now, let’s examine how Confidential Matching using TEEs works and whether the issues are addressed adequately.
Simplified flow diagram of Confidential Matching
As you can see from the illustration above, both the advertiser and Google upload their customer data to the TEE, where the matching of the two data sources is orchestrated. The output of this matching process is only the data points for which there is a match in Google’s database. These matches are then made available in the GMP as audiences or for reporting purposes. In essence, Google cannot learn any new data from the advertiser since this is prohibited by the TEE’s functionality.
Conclusion
Confidential Matching seems to address a key issue: excessive data sharing. It offers a solution where only necessary, matched data is exposed, helping advertisers meet growing privacy requirements. By leveraging Trusted Execution Environments, advertisers can trust that Google won’t see or store their raw customer data.
However, while this technical solution tackles data security, it doesn’t fully address purpose limitation—that is, whether Google might still use this matched data for purposes beyond retargeting or measurement. E.g., even if the advertiser ensures user consent for retargeting, once the data is in Google’s system, it’s unclear whether Google might utilize this data in ways the advertiser’s users haven’t explicitly agreed to. Now, one might argue that since Google had access to this data all along and (ideally) obtained user consent for their own usage purposes, the advertiser shouldn’t be concerned with this. But given Google’s vast ecosystem, this remains a valid concern for businesses mindful of privacy compliance.
Moreover, advertisers using Confidential Matching still bear the legal and reputational risks if privacy regulations evolve or if regulators decide that these technical solutions aren’t compliant. For instance, privacy watchdogs (e.g., NYOB) might as well demand more guarantees about how matched data is used downstream.
In light of these concerns, Confidential Matching is a step forward—but not a solution that should lead advertisers to blindly adopt it. It addresses data security but leaves room for doubt regarding the actual handling of data post-match. As privacy is top of mind for many users, businesses will still need to carefully evaluate their legal obligations, privacy risk profiles, and trust in Google’s systems.
I hope you found this article insightful and that you have a better understanding of Google’s new Confidential Matching feature. If you have any further questions or need help handling the implementation or discussions internally, feel free to contact me via one of the channels listed on this website. Happy tracking!
References
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