The question arises, when Machine Learning is not private? What can be done to protect your data? What if someone steals your data? What if your machine learning algorithms use social media data to predict your medical history? The answer is yes.
Machine learning requires massive amounts of data, often from social networks. However, there are ways to protect your data and keep it private. First, think about the privacy implications of the data you collect.
A simple NLP model may have gradients that could reveal the words of a user. However, if the user were to recover these words, they would need access to the whole dataset. In that case, privacy would not be protected. A dense model would present a greater challenge to attackers. The following is a short list of possible methods for implementing differentially private machine learning. These methods are conceptually simple and supported by several open source frameworks.
In order to protect your private data, use encryption. This is especially important if you use CNNs, as their many gradients make them more vulnerable to attack. For example, if an attacker retrains the model based on an anonymous dataset, they can use those data to get private data. However, it’s important to remember that even if you’ve made your data private, the attacker can still retrieve it with some other method, by using the generic model.
In contrast, differentially private machine learning is an approach that limits the leakage of data by ensuring that the model’s training data is kept private. This method also formalizes the notion of differential privacy by limiting the amount of data that can be used for training. This prevents the model from accidentally storing sensitive data. This technique is especially useful for machine learning that relies on social media data. It has a number of benefits.
The sample and aggregate framework adds differential privacy to non-private algorithms. It’s model-agnostic and applies to multi-class classification problems. The training data is partitioned into k disjoint subsets and independent models are trained on each subset. Then, the test example x is computed from a private histogram of the predicted predictions. It also reduces the computing cost and prevents overfitting.
The question of privacy in machine learning has been raised by numerous cases of bias and discrimination. While companies usually have good intentions when automating processes, the unforeseen consequences of AI in hiring practices has been highlighted by Reuters. Amazon recently automated its hiring process and unintentionally discriminated against job applicants based on gender. As a result, the company scrapped the project. Once this problem was realized, many other companies have followed suit.
Another method that addresses data privacy and secrecy is federated learning. This technique allows the training of machine learning models on data stored on various devices and servers. Instead of uploading local data samples to a central server, it trains a generic model on the devices of users – the majority of mobile devices. Once trained, the model is then shared back to the main server. This process also makes it more difficult to hack into.
In contrast, the student model can be trained on private data and applied to a public dataset. In this way, it can be used to predict the future. This is a highly effective technique for classifying data from public datasets. It also allows for a constant privacy budget. In this way, private data is used to train a student model, and public data is used to create a new classifier. The student model can have any architecture, and it can be queried as often as required.
However, when the data comes from the personal devices, it may still end up in the hands of passive attackers. In this situation, federated learning is beneficial for the user because it allows a personalized experience, as the models are built based on real user data. This saves bandwidth and is also more private than using generic models. Moreover, it doesn’t involve sending private data to a central server, which still involves privacy breaches.