Confidential AI Solutions for Better Adtech Privacy & Analytics
Challenges
Adtech companies often handle large amounts of sensitive data, which can make them a target for cyber attacks. This poses a risk to consumers’ data privacy if the security measures are not sufficient.
Approaches such as data clean rooms and encryption solutions only offer a narrow level of data protection and privacy. However, they have limitations that restrain their use and effectiveness for useful data analytics in advertising.
- Data clean rooms can be expensive to implement and maintain, and are vulnerable to privacy leaks or theft.
- The explosive growth of confidential data, as well as the need to protect it, creates obstacles to analytics in advertising, at scale.
- A bottleneck in analytics and machine learning leaves much of this valuable data stuck in silos, unable to be accessed and harnessed.
- Approaches such as homomorphic encryption suffer from scalability and speed limitations that restrain the size and type of analytics that can be performed on the data.
- Alternative solutions are susceptible to side channel attacks and other exploits.
The Opaque Solution
Opaque AI-driven Platform empowers AdTech companies to unlock the value of confidential and sensitive data by enabling useful analytics and machine learning on fully encrypted data at scale.
This ensures encryption not just at rest and in transit, but in use as well – allowing you insights from valuable data without compromising advertising privacy or security. Opaque functions as a secure data room that is different in many significant ways, including:
- Providing cloud native operation that executes directly within a secure enclave on your cloud instance (e.g. Azure), effectively limiting data movement.
- Supporting advanced analytics and AI and machine learning modeling at scale.
- Enabling scalable execution of standard analytic tools and approaches, including Spark.
- Furnishing a powerful policy framework to govern permissioning and access.
- Integrating with all your key analytics and data providers.
Confidential computing is a game-changer for the adtech industry. It allows us to securely share and analyze data without compromising privacy, which is essential for maintaining the trust of our users.
Use Cases
- Optimize Matching Results
Advertisers seeking to leverage their sensitive first-party data for targeting and modeling work to quickly “bump” datasets together and discover the overlap.
- Target High-Performing Customers
With the ability to better collaborate and analyze shared data, you can train AI and machine learning models to determine those customers most likely to convert. Gaining a better understanding of your audience and their stage within the buyer journey enables you to create more relevant ads. And all this without exposing sensitive or private data.
- Personalize Messages and Offers
Being able to expand on the targeting information allows you to fine-tune offers and messages to individual customers, boosting conversions and advertising returns. The ability to perform extensive analytics across diverse data sets, including sensitive data, is your key to better personalization and results.
- Increase Monetization
When it comes to optimizing campaigns, clients are always looking for ways to improve their results. Opaque enables analysis across digital exposure data to help you better understand customer engagement and improve your in-campaign optimization.
- Speed Time-to-Market
Opaque eliminates data processing and coordination delays, speeds analysis and ultimately enables you to get more effective solutions to market more quickly. Data enrichment partners can determine more precise entity resolution faster – without compromising personal data.
- Refine Campaign Measurement
The key to digital marketing is continuous measurement and improvement of campaigns. Opaque empowers better analytics with AI and machine learning to fine-tune targeting and offers.
Publishers and clients also want to know if a partnership is effective. By combining first-party data with exposure data from publisher partners, the factors driving the specifics of advertising returns can be better understood, and future spend and partnerships can be improved.
See Opaque in action and confidential analytics and AI use cases across AdTech, Finance, Insurance, Healthcare and more.
