Privacy is a core pillar of healthcare advertising, and it's a particularly hot topic at the moment. It's important for both pharma marketers and consumers to know that any health data is well-protected and in the right hands. Thankfully for pharma marketers, who are already required to follow stringent regulations like HIPAA, there is differential privacy. This technology can significantly improve privacy standards while analyzing datasets. To understand the benefits, it helps to understand the basics: what is it, and how does it work?
What Is Differential Privacy?
In 2006, four computer scientists published an article introducing the concept of differential privacy. They initially defined the concept as a mathematical definition for privacy loss associated with data extracted from a statistical database. This research examined and proposed a process that would protect the privacy of individuals within a dataset while still successfully extracting useful information from that dataset.
Differential privacy works by adding pre-determined randomness into a computation performed by a machine-learning algorithm. This creates a certain level of "noise," adding extra information to the dataset to improve privacy but not to alter its findings.
It's particularly useful when applied to large datasets, for example as part of online advertising, where a single individual's information is not a necessary part of the equation. If a dataset consists of two people, each person would contribute 50% to the dataset. However, in a dataset of 200 people, each individual represents just 0.5% of that dataset. In other words, the technology enables algorithms to extract critical insights and generalized learnings from datasets without linking that information to a specific individual within the dataset.
There are two approaches to differential privacy. Central differential privacy refers to noise added to a dataset that has already been collected as part of its analysis. Here, the entity aggregating the data strips identifying information before aggregating the dataset, then inserts noise to reinforce its privacy measures. Local differential privacy, on the other hand, refers to noise added to a dataset on a device before the data is ever collected or received by the party doing the aggregating and analyzing.
Four Real-World Examples
Differential privacy is a complicated concept with roots in cryptography. To truly understand how it works, it helps to see real-world examples. Here are four of them:
- Browsing history. Google introduced Randomized Aggregatable Privacy-Preserving Ordinal Response to Chrome in 2014. The search giant is able to collect probabilistic information about users' browsing habits, drawing insights about how people interact with their browsers while protecting their sensitive information. Five years later, Google open-sourced its differential privacy, helping other companies study their own data trends in a privacy-safe way.
- App usage. How do people use their smartphone apps? Local differential privacy helps Apple understand, by adding noise to individual data before receiving it back. This includes everything from emoji usage to health information from the iOS Health app. With noise inserted locally, Apple can evaluate this information while ensuring it can't be traced back to a specific person.
- Healthcare advertising. Differential privacy enables healthcare advertisers to build lookalike models for advertising campaigns, assessing the demographics that might be relevant for a campaign and targeting audiences that fit a similar profile. This allows advertisers to target messaging without relying on anyone's sensitive information, protecting individual privacy while going beyond baseline HIPAA compliance.
- Understanding census data. The U.S. Census Bureau started using the technology in 2020. The bureau reports the population of a state and the number of housing units in each census block as-is, but injects noise elsewhere so demographic information can't be used to reveal anyone's identity from the anonymized datasets.
Importance of Differential Privacy in Healthcare Advertising
Healthcare advertisers work under tighter constraints than those of almost any other industry, since the data they rely on often touches real patients and real health conditions. Differential privacy gives marketers a way to build accurate, useful audience models from that data without exposing any single patient's information in the process. That matters both for regulatory reasons, since it supports compliance beyond the baseline HIPAA requires, and for the simpler reason that patients and providers need to trust a brand isn't handling their health information carelessly. Without a technique like this, advertisers would face a much starker choice between precision and privacy.
Understanding the Tradeoff Between Privacy and Data Utility
Differential privacy isn't free. Adding more noise to a dataset increases privacy protection, but it also makes the underlying data less precise, which is why practitioners talk about a "privacy budget" that has to be calibrated rather than maximized blindly. Too little noise and individual patients become identifiable again. Too much noise and the insights an advertiser pulls from the data stop being reliable enough to act on. Getting that balance right, dataset by dataset, is what separates a differential privacy implementation that actually protects people from one that just adds friction without adding safety.
How DeepIntent Leverages Differential Privacy for Safety and Compliance
DeepIntent applies differential privacy across its platform to build patient and provider audiences that are useful for targeting without ever exposing an individual's underlying health data. That approach lets campaigns reach the right people with real precision while keeping compliance and patient trust intact, rather than treating those as competing priorities. To understand how DeepIntent leverages differential privacy to reach patient audiences in a privacy-safe way, click here, or schedule a demo with our team to see the approach in action.
