In an era when data is everything, everywhere, all at once and computation has almost no limit, ensuring privacy while leveraging data analytics is paramount. The US Department of Commerce’s National Institute of Standards and Technology (NIST) recently published NIST Special Publication 800-226 (the Guidelines), a comprehensive guide for evaluating and achieving differential privacy, a cutting edge approach to protecting individual privacy when using and relying on large datasets.
The Importance of Differential Privacy
Differential privacy is a framework that relies on mathematical rigidity to create robust privacy of individual data. This robustness makes it resistant to privacy attacks, including those not yet developed, offering a high level of protection for individual data. As such, differential privacy offers significant advantages to previous privacy techniques, such as de-identification, which can be reverse engineered; in fact, differential privacy is used by entities such as the US Census Bureau when releasing census data.
Methods to Achieve Differential Privacy
Achieving differential privacy involves adding random “noise” to results, balancing privacy and utility. NIST outlines the differential privacy pyramid, a standard by which persons or entities seeking to implement differential privacy can understand the necessary levels of protection required by the Guidelines. From top to bottom, the differential privacy pyramid identifies the importance of:
- The Top: Setting privacy parameters and identifying the unit of privacy to measure the strength of the privacy “guarantee”
- The Middle: Identifying algorithms and correctness, ways to measure the utility of the model, and ways in which algorithms could introduce bias
- The Bottom: Focusing on access controls, trust models, side channels, data collection, and other complementary privacy approaches
Although NIST is striving for standardization, a particular differential privacy model is created or tailored by engaging various stakeholders to determine the relevant requirements.
Challenges in Implementing Differential Privacy
Implementing differentially private algorithms is not without challenges. One significant hurdle is the complexity of random sampling, which requires substantial expertise among internal stakeholders. The privacy-utility tradeoff further complicates implementation—adding more noise to a dataset degrades the dataset’s ability to be used in coherent analysis. Therefore, more robust privacy models may come at the cost of less accurate or useful results from the protected dataset. Entities seeking to utilize differential privacy must decide which spot on the privacy-utility spectrum best suits the needs of their organization’s operations, objectives, compliance regime, and risk tolerance.
Differential Privacy – A Difference Maker?
The Guidelines provide valuable insights into the methods, challenges, and practical recommendations for implementing differential privacy. By understanding and applying these guidelines, practitioners can better navigate the complexities of privacy protection and data utility, ensuring that privacy remains a priority in data analytics and sharing while expanding opportunities for collaboration and discovery as datasets and artificial intelligence models continue to grow.
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