Differential privacy is a technique for protecting the privacy of individuals when their data, such as personal information or medical records, is used for research or analysis. Machine learning models trained on sensitive data can compromise individual privacy, so researchers have proposed methods to train these models while providing privacy guarantees.
PATE (Private Aggregation of Teacher Ensembles) is a differential privacy method that trains multiple teachers on private data and then uses the models to train a student model, allowing the student model to learn from the private data without compromising data privacy. Traditional PATE methods provide a global privacy guarantee for the entire data set, but do not ensure that the privacy of every individual in the data set is protected. This is particularly important when the data set contains sensitive information about people, such as medical or financial data. Recently, a new paper titled “Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees” was published to present a method for training machine learning models on sensitive data that guarantees differential privacy for each individual in the data set. This extension of the PATE method provides a global privacy guarantee for the entire data set.
The proposed method for individualized PATE trains multiple teachers on different subsets of data and then averages the teacher’s predictions to obtain a final model. The method uses the concept of differential privacy to ensure that private data is not compromised. The method also requires the use of a secure multi-party computing (MPC) protocol for aggregation of teacher predictions.
Specifically, the authors proposed to start by dividing sensitive data into multiple unconnected subsets and training multiple teachers on each subset. These teachers are trained in private data but do not have access to the data themselves. Instead, they are given a differentially private summary of the data, allowing them to make predictions about the data without compromising people’s privacy. Once the teachers are trained, they make predictions in a separate validation set. These predictions are then aggregated using a secure multipart computing (MPC) protocol to obtain the final model. The MPC protocol ensures that the predictions are combined in a way that preserves the privacy of the individuals in the data set. The final model is a combination of the predictions made by various teachers and can learn from the private data without compromising the privacy of the data.
An experimental study was carried out on multiple data sets to demonstrate the effectiveness of the proposed method. Experiments were performed on multiple data sets, including real-world and synthetic data sets. The authors used differentially private versions of models known as logistic regression and neural networks as teachers. The results obtained show that the method can achieve accurate predictions while providing individual privacy guarantees. In addition, research shows that this new approach offers greater privacy guarantees compared to traditional PATE methods, as it ensures that the privacy of every individual in the data set is protected, regardless of the presence of other individuals in the data set. data.
In this paper, we present a novel approach, individualized PATE, which provides stronger privacy guarantees than traditional PATE methods, as it ensures that the privacy of every individual in the data set is protected, regardless of the presence of other individuals in the data set. the data set. The experimental results demonstrate that the method can achieve accurate predictions while providing individual privacy guarantees. However, it requires the use of a secure multi-party computing (MPC) protocol for the aggregation of teacher predictions.
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Mahmoud is a PhD researcher in machine learning. He also has a
bachelor’s degree in physical sciences and master’s degree in
telecommunication systems and networks. Your current areas of
the research concerns computer vision, stock market prediction and
learning. He produced several scientific articles on the relationship with the person.
identification and study of the robustness and stability of depths
networks
