Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Real world images often contain large amounts of private / sensitive information that should be carefully protected without reducing their utilities.
In this paper, we propose a privacy-preserving deep learning framework with a learnable ob- fuscator for the image classification task.
Our framework consists of three mod- els: learnable obfuscator, classifier and reconstructor.
The learnable obfuscator is used to remove the sensitive information in the images and extract the feature maps from them.
The reconstructor plays the role as an attacker, which tries to recover the image from the feature maps extracted by the obfuscator.
In order to best protect users’ privacy in images, we design an adversarial training methodol- ogy for our framework to optimize the obfuscator.
Through extensive evaluations on real world datasets, both the numerical metrics and the visualization results demonstrate that our framework is qualified to protect users’ privacy and achieve a relatively high accuracy on the image classification task.
In the past few years, deep neural networks (DNNs) have achieved great breakthroughs in computer vision, speech recognition and many other areas.
To support the training of DNNs, large datasets have been collected, e.g., ImageNet BID6 , MNIST (LeCun et al., 1998) and CIFAR-10/CIFAR-100 BID15 ) as image datasets, Youtube-8M (Abu-El-Haija et al., 2016) as video datasets, and AudioSet BID8 as audio datasets.
These datasets are usually crowdsourced from the real world, and may carry sensitive private information, thus, leading to serious privacy problems.The new European Union's General Data Protection Regulation (GDPR) (Regulation, 2016) stipulates that personal data cannot be stored for long periods of time, and personal data requests, such as deleting personal images, should be handled within 30 days.
In other words, this regulation prevents long-term storage of video/image data (e.g., from CCTV cameras), which hinders the collection of real-world datasets for training deep learning models.
However, the data storage limitations do not apply if the data is anonymized.This regulation considers the trade-off between the utility and the privacy of the data.
However, 30 days may not be a long enough period to collect image data and train a complex deep learning model, and deletion of data hinders re-training later when the model structure is updated or more data becomes available.
GPDR allows anonymized data to be stored indefinitely, which inspires us to design a framework where an image is converted into an obfuscated intermediate representation that removes sensitive personal information while retaining suitable discriminative features for the learning task.
Thus the obfuscated intermediate representation can be stored indefinitely for model training in compliance with GDPR.
Contributions In this paper, we design a obfuscator-adversary framework to obtain a trainable obfuscator that fulfills the dual goals of removing sensitive information and extracting useful features for the learning task.
Here, we mainly focus on image classification as the learning task, since it is a more general task in computer vision -the framework could be extended to other tasks.
Our framework consists of three models, each with its own objective: the obfuscator, the classifier and the reconstructor, shown in Figure 1 .
The obfuscator works as an information remover, which takes the input image and extracts feature maps that carry enough primary information for the classification task while removing sensitive private information.
These feature maps are the obfuscated representation of the input image.
The classifier uses the obfuscated representation to perform classification of the input image.
Finally, the reconstructor plays the role as an adversary whose goal is to extract the sensitive information from the obfuscated representation.
Privacy Attack:Step 1:
We proposed a deep learning framework on privacy-preserving image classification tasks.
Our framework has three modules, the obfuscator, classifier, and reconstructor.
The obfuscator works as an feature extractor and sensitive information remover to protect users' privacy without decreasing the accuracy of the classifier.
The reconstructor is an attacker, and has an opposite objective to reveal the sensitive information.
Based on this antagonism, we designed an adversarial training methodology.
Experiments showed our framework is qualified to protect users' privacy and achieve a relatively high accuracy on the image classification task.
We proposed a novel deep learning image classification framework that can both accurately classify images and protect users' privacy.