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S-GBDT: Frugal Differentially Private Gradient Boosting Decision Trees
For the Abalone dataset for $\varepsilon=0. 54$ we achieve $R^2$-score of $0. 47$ which is very close to the $R^2$-score of $0. 54$ for the nonprivate version of GBDT.
The Applicability of Federated Learning to Official Statistics
This work investigates the potential of Federated Learning (FL) for official statistics and shows how well the performance of FL models can keep up with centralized learning methods. F L is particularly interesting for official statistics because its utilization can safeguard the privacy of data holders, thus facilitating access to a broader range of data.
Lessons Learned: Defending Against Property Inference Attacks
Extensive experiments with property unlearning show that while it is very effective when defending target models against specific adversaries, property unlearning is not able to generalize, i. e., protect against a whole class of PIAs.
Trustworthy AI Inference Systems: An Industry Research View
Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time.
