One of the most common misconceptions about data clean rooms and data collaboration is that there are requirements on having tons of identified data. 

Most publishers we meet have this concern:  “Do we really have enough data to drive significant revenue? Won’t we be limited by the size of the match, and therefore won’t be able to run any media at scale?“

Typically they are surprised to learn that mitigating low volumes of identified data is part of the solutions offered today by this class of data collaboration technology.

No matter how little identified data any given publisher has, they can benefit from growth using data collaboration technologies. The reason is quite simple:  any campaign is better off when it starts with real data. 

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Unlocking Audience Insights and Prospecting Powers

Following a match with an advertiser, the publisher has a few options: one, a simple one, is simply to have insights on the matched audience. The publisher can better understand the brand’s customers or prospects as a function of their own data, which in turn allows them to create better media products.  It also shows the brand that the publisher reaches the right audience for them.  Insights are offered as a report that provides aggregate numbers – by definition, it is a privacy-safe product. 

The second, and an important one, is the possibility of creating a prospecting audience out of the match.  Optable’s prospecting clean room app automatically creates an expanded audience that provides scale, performance and value when it comes to reaching the right audience.  Not only that, but we do it in a privacy-safe manner, since the publisher does not learn the intersection – only the prospecting audience becomes eligible for targeting. 

Considering that a publisher’s audience consists of both identified and unidentified users who share a number of traits, Optable prospecting clean room app allows a publisher to configure a model that ultimately creates an addressable audience that is sizable enough to drive significant growth. 

For brands, the use of customer or prospect data also doesn’t have a limiting factor – in fact, there are few brands that can boast having significant data on all their customers. For everyone else, the objective is to have some data – enough to allow our systems to make better audience decisions. 

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Optable’s approach

We make publisher-driven data collaboration easy for all parties:  our end-to-end solution includes direct integration for activation straight from the clean room environment, and offers frictionless interoperability. 

Given the emergence of retail media and the democratization of data through data warehouse clean room APIs, data collaboration is quickly becoming a major revenue opportunity. 

Forward-looking publishers who are looking for revenue growth must prioritize future-proof, privacy-safe solutions to driving revenue.

Differential Privacy has emerged as a powerful technique to protect individual privacy while still reaping the benefits of data-driven insights. In this blog, we’ll explore differential privacy, how it works, and how media companies can use it to safeguard sensitive data about consumers. 

What is Differential Privacy?

Differential privacy is a privacy-enhancing technology (PET) that allows organizations to analyze data while preserving the privacy of individual people. The core principle is to ensure that no specific piece of information about an individual can be inferred from the results of a query or analysis. This means that the results of the analysis will look nearly the same regardless of whether any single individual's information was included in the analysis or not.

How Does Differential Privacy Work?

Differential privacy is achieved by adding carefully curated random noise to the dataset at a high enough rate that it protects privacy, but not so high that it diminishes utility. This can be achieved in two ways:

1. Randomized Responses: Data is intentionally perturbed or randomized to introduce uncertainty into the results. This means that the output of a query or analysis is not an exact representation of the raw data, but rather a noisy version. For example, when queried about individuals’ interest in sports, a differentially private system would randomly report either "yes" or their true reponse.
2. Noisy Aggregates: Differential privacy is often used in situations where data is aggregated, and reported as noised group summaries. This ensures that no specific individual's information can be inferred. For example, if 353 individuals are interested in sports, a differentially private system would add random noise and report it as 347 or 360.

Any statistical analysis, whether using differential privacy or not, still leaks some information about the end users whose data are analyzed. As more and more analyses are performed on the same individuals or end users, this privacy loss can quickly accumulate. Fortunately, differential privacy provides formal methods for tracking and limiting this cumulative privacy loss.

How are Media Companies Using Differential Privacy?

Differential privacy facilitates secure data sharing among media organizations and marketers, promoting collaboration without compromising any individual’s privacy. This technology is particularly helpful when companies are trying to gather consumer insights from:  

• Location-Based Services: Companies use differential privacy to aggregate and analyze location data from mobile devices without exposing the exact whereabouts of individual users.
• Machine Learning: Differential privacy is used to train machine learning models on sensitive data while ensuring that the models do not memorize individual records.
• Campaign Analytics: Social media platforms and publishers employ differential privacy to report performance insights from an ad campaign, analyze user behavior and identify  trends without compromising any individual user’s privacy. 

When brands and media companies use differential privacy as one of their PETs, it helps them comply with data privacy regulations as well as build trust with consumers by assuring them that their data is handled with care. 

As data continues to play an essential role in finding and retaining user interest, media companies must implement differential privacy to harness data-driven insights while respecting individual privacy rights. It is poised to be an integral part of data analytics and sharing in a privacy-conscious world.