Differential Privacy Advances in Machine Learning

The field of machine learning is moving towards greater emphasis on privacy and security, with a particular focus on differential privacy. Researchers are developing new techniques and algorithms to ensure that machine learning models can be trained and deployed while protecting sensitive data. One of the key directions is the development of differentially private algorithms for tasks such as bandits, federated learning, and optimization problems. These algorithms aim to provide strong privacy guarantees while maintaining the performance of the models. Another area of research is the development of new methods for private data release, including techniques for releasing multiple datasets with different privacy parameters. Additionally, there is a growing interest in applying differential privacy to real-world applications, such as clinical data analysis and fair learning. Notable papers in this area include:

  • PrivATE, which presents a novel framework for computing differentially private confidence intervals for average treatment effects.
  • Faster Rates for Private Adversarial Bandits, which introduces new algorithms for private bandits with improved regret bounds.
  • Private Lossless Multiple Release, which develops methods for lossless multiple release of private datasets.
  • Private Rate-Constrained Optimization with Applications to Fair Learning, which studies constrained minimization problems under differential privacy.

Sources

PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects

Faster Rates for Private Adversarial Bandits

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning

Stochastic Primal-Dual Double Block-Coordinate for Two-way Partial AUC Maximization

Inclusive, Differentially Private Federated Learning for Clinical Data

Private Lossless Multiple Release

Private Rate-Constrained Optimization with Applications to Fair Learning

Buffer-free Class-Incremental Learning with Out-of-Distribution Detection

Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model

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