Cutting through the noise: Explaining differential privacy and why it matters

From word usage predictions on smartphone keyboards to producing country-wide birth estimates, differential privacy has been applied to many use cases across industries. Differential privacy can help facilitate data analysis on sensitive datasets across organizations and borders and can minimize exposure to sensitive data in the event of a breach. This important privacy definition will certainly see wider application and scrutiny in the artificial intelligence era — for example, to address risks of large language models memorizing private information during training. Its adoption and applicability across many domains of digital risk speaks to its value. Despite progress from theory to practice, differential privacy is often characterized, if not dismissed, as a niche topic for many privacy professionals.

Differential privacy has notable strengths, but it is not a cure-all for privacy protection. Its use requires careful weighing of a tradeoff between privacy and data accuracy/utility, and there is a lack of agreement around what levels of noise infusion are required to protect privacy in practice. Common criticisms of differential privacy are once again making headlines and appear to be driving policymaking restricting its use. These developments merit a closer look at the benefits and drawbacks of this important privacy definition, the implications of use restrictions and the role of metrology in advancing the field's maturity.

Differential privacy basics

Differential privacy places tunable bounds on how much any individual record contributes to a data analysis. Unlike related techniques such as coarsening or suppression, differential privacy is a definition. To meet the definition of differential privacy, a calibrated amount of random noise is added to whatever process generates output data from an input dataset. A well-calibrated, differentially private survey will provide a respondent with plausible deniability from having participated.