More security for 'machine learning' technology in the cloud
A project in which MIT scientists work could provide more efficient security in machine learning in the cloud and also involves a promising approach to the use of cloud-based neural networks for the analysis of medical images and other applications They use critical data.
It is a system that combines two conventional techniques (homomorphic encryption and distorted circuits) so that it helps networks execute orders of magnitude faster than they do with conventional approaches.
As they explain from MIT, cloud administrator outsourcing machine learning is an upward trend in the industry. “The main technology companies have launched cloud platforms that perform complicated computing tasks, such as running data through a convolutional neural network (CNN) for image classification. Small businesses with limited resources and other users can upload data to those services in exchange for a fee and get the results in several hours, ”they say. However, what happens if private data leaks occur? "In recent years, researchers have explored several secure computing techniques to protect these critical data - they continue from the technological institute." But these methods have performance disadvantages that make the evaluation of the neural network (testing and validation) slow (sometimes up to a million times slower) which limits its wider adoption. ” A problem that could be solved with the aforementioned project, presented a few days ago at the USENIX security conference .
The system in question, called Gazelle , can be used in two-part image classification tasks. It would work like this: a user sends encrypted image data to an online server that evaluates a CNN running on Gazelle. After this, both parties share encrypted information back and forth to classify the user's image. Throughout the entire process, the system ensures that the server never learns any loaded data, while the user never learns anything about the parameters of the network. However, compared to traditional systems, Gazelle works between 20 and 30 times faster than the latest generation models , while reducing the required network bandwidth by an order of magnitude.
It is a system that combines two conventional techniques (homomorphic encryption and distorted circuits) so that it helps networks execute orders of magnitude faster than they do with conventional approaches.
As they explain from MIT, cloud administrator outsourcing machine learning is an upward trend in the industry. “The main technology companies have launched cloud platforms that perform complicated computing tasks, such as running data through a convolutional neural network (CNN) for image classification. Small businesses with limited resources and other users can upload data to those services in exchange for a fee and get the results in several hours, ”they say. However, what happens if private data leaks occur? "In recent years, researchers have explored several secure computing techniques to protect these critical data - they continue from the technological institute." But these methods have performance disadvantages that make the evaluation of the neural network (testing and validation) slow (sometimes up to a million times slower) which limits its wider adoption. ” A problem that could be solved with the aforementioned project, presented a few days ago at the USENIX security conference .
The system in question, called Gazelle , can be used in two-part image classification tasks. It would work like this: a user sends encrypted image data to an online server that evaluates a CNN running on Gazelle. After this, both parties share encrypted information back and forth to classify the user's image. Throughout the entire process, the system ensures that the server never learns any loaded data, while the user never learns anything about the parameters of the network. However, compared to traditional systems, Gazelle works between 20 and 30 times faster than the latest generation models , while reducing the required network bandwidth by an order of magnitude.
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