Artificial intelligence, or AI, has seen rapid advancements with the advent of large language models such as ChatGPT and generative image technology such as Adobe Firefly.

As AI advances, the need for data has become more essential. AI runs on algorithms that need to be “trained” on robust datasets to learn how to properly function.

Some datasets involve data collected from many people, known as aggregate data, which can contain sensitive information, from names and dates of birth to shopping habits, medical history and even political preferences. Examples include using AI to provide insights about health outcomes among specific demographics or identifying patterns in census data for civic resource management.

As AI researchers share their work with each other or institutions fall victim to hacks that steal data, there’s an increasing risk of spreading private information that could be harmful or illegal. Current methods of privacy protection require sacrificing accuracy or vice versa, leaving no optimal solution for accurate AI algorithm outputs while protecting data privacy.

Oliver Kosut, an associate professor of electrical engineering in the Ira A. Fulton Schools of Engineering at Arizona State University, is addressing the issue by analyzing the best ways to balance accuracy and privacy considerations when training AI algorithms.

Kosut and his collaborators, who include Lalitha Sankar, a Fulton Schools professor of electrical engineering, and Flavio du Pin Calmon, an assistant professor of electrical engineering at Harvard University, are investigating four areas of focus to ensure optimal differential privacy, which provides accurate insights into datasets without using unnecessary personal information.

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