Written by Dylan Hart
As Product Director at a media measurement company, Dibjot Singh, is at the forefront of developing systems that prioritize data protection. His work on identity graphs, advanced digital on-target rate measurement methodology, and clean rooms has introduced methods for secure data processing while maintaining the precision of media measurement.
Innovation in Multi-Source Identity Graphs
Drawing from more than 10 years of experience in data science and advertisement technology, Singh identified a critical gap in the advertising industry: the need for privacy-safe identity resolution that could work across platforms without exposing sensitive data. This insight led him to design the multi-sourced identity graph, a tool that combines data from multiple sources to create a more accurate framework for media measurement. As traditional digital identifiers become less effective, this system offers a privacy-compliant alternative for audience targeting and measurement.
“The multi-sourced identity graph allows us to integrate multiple data points without compromising privacy,” Singh explains. “It was designed to handle the demands of the evolving ad tech space while respecting the highest standards of data protection.”
Working with his engineering teams, Singh led the development of this innovative solution from concept to implementation.This innovation has greatly enhanced the company’s ability to measure campaign performance, reducing audience duplication by 22% and improving targeting accuracy, resulting in a 15% increase in return on ad spend for several clients. This novel solution represents a significant advancement in how advertisers can understand audience behavior across platforms while protecting consumer privacy.
Through his work on multi-source identity frameworks, Singh has contributed to improving campaign measurement methodologies across digital platforms. Recognizing evolving advertiser needs, his approach helped enhance measurement processes to provide clearer insights into campaign performance. This work has supported advertisers in making more informed decisions about resource allocation across digital and linear platforms, while maintaining robust privacy compliance throughout the measurement process.
Secure Compute: The Technical Backbone
Singh’s work also involves designing a technical infrastructure that enables secure data processing. His contributions have led to the development of a system that allows clients to run complex data analyses without exposing or storing sensitive information. This secure compute model underpins both the multi-sourced identity graph while accurately measuring on target rates for digital audiences, providing clients with the tools they need while ensuring data privacy is maintained.
“We’ve built a system that processes data safely, enabling clients to conduct in-depth analyses without fear of breaching privacy standards,” Singh explains. This technical foundation has been instrumental in maintaining the company’s reputation as a leader in privacy-first advertising technology.
Developing Privacy-Focused Data Sharing Solutions
Building on this secure compute foundation, Singh played a key role in the implementation of clean rooms as vital tools for processing identity data in the advertising sector. These controlled environments allow users to analyze sensitive information without exposing it to external parties. Under Singh’s leadership, the company’s clean rooms facilitate secure collaboration between advertisers and media owners, enabling them to match audience data using personally identifiable information (PII) while maintaining strict privacy controls.
“Our clean rooms ensure that data is processed without risk,” says Singh. “This system allows our clients to extract actionable insights while keeping their data safe.”
Singh’s clean room implementations have supported the company’s efforts to handle large datasets while ensuring compliance with data privacy regulations. The use of double-blind joins allows data to be analyzed for campaign effectiveness, audience overlap, and cross-platform measurement without exposing sensitive details.
Shaping the Future of Data-Driven Advertising
As privacy regulations become stricter, Singh’s innovations are paving the way for a more responsible approach to data processing in advertising. The multi-sourced identity graph, digital on-target rate measurement, and clean rooms have helped the company deliver better-targeted campaigns while maintaining compliance with privacy laws.
Singh’s leadership has driven a significant increase in client adoption of these privacy-first technologies, reflecting the growing demand for solutions that combine security with performance. “We’ve made privacy an integral part of how we work with data,” says Singh.
With these innovations, Singh continues to shape the future of ad tech, offering solutions that meet both the needs of the market and the expectations of consumers in an increasingly privacy-conscious world.
