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Balancing Data Utility and Privacy: How Differential Privacy Benefits Media Companies

December 21, 2023
Akshaya Mani
Blog
PETs
Data Governance & Privacy

Balancing Data Utility and Privacy: How Differential Privacy Benefits Media Companies

December 21, 2023
Akshaya Mani
Blog
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Balancing Data Utility and Privacy: How Differential Privacy Benefits Media Companies

December 21, 2023
Akshaya Mani

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.