New Algorithms for Differentially Private Stochastic Convex Optimization
Differentially private stochastic convex optimization (DP-SCO) for user-level privacy is a hot topic in the world of Artificial Intelligence. Existing work either takes too long or needs a lot of users to work properly. In response, researchers have developed new algorithms. These algorithms are faster, need fewer users, and work well with non-smooth functions. They are based on multiple-pass DP-SGD and use a private mean estimation procedure for concentrated data.
The significance of user-level DP-SCO
User-level DP-SCO is crucial for protecting the privacy of individual users in AI applications. It is important to have algorithms that can handle this type of optimization without taking too long or requiring a massive number of users.
Optimal rates, polynomial time, and logarithmic growth
The new algorithms for user-level DP-SCO are groundbreaking because they achieve optimal rates, run in polynomial time, and only need a number of users that grow logarithmically in the dimension. This is a big improvement over existing methods and opens up new possibilities for AI applications.