scholarly journals GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 58
Author(s):  
Jinao Yu ◽  
Hanyu Xue ◽  
Bo Liu ◽  
Yu Wang ◽  
Shibing Zhu ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Personal Information ◽  
Image Data ◽  
Generative Adversarial Networks ◽  
The Internet ◽  
Adversarial Networks ◽  
Massive Number ◽  
Image Utility

With the development of the Internet of Multimedia Things (IoMT), an increasing amount of image data is collected by various multimedia devices, such as smartphones, cameras, and drones. This massive number of images are widely used in each field of IoMT, which presents substantial challenges for privacy preservation. In this paper, we propose a new image privacy protection framework in an effort to protect the sensitive personal information contained in images collected by IoMT devices. We aim to use deep neural network techniques to identify the privacy-sensitive content in images, and then protect it with the synthetic content generated by generative adversarial networks (GANs) with differential privacy (DP). Our experiment results show that the proposed framework can effectively protect users’ privacy while maintaining image utility.

scholarly journals GANs Based Density Distribution Privacy-Preservation on Mobility Data

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Dan Yin ◽  
Qing Yang
Keyword(s):  
Density Distribution ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Personal Information ◽  
Original Data ◽  
Location Based Services ◽  
Generative Adversarial Networks ◽  
Published Data ◽  
Mobility Data ◽  
Adversarial Networks

With the development of mobile devices and GPS, plenty of Location-based Services (LBSs) have emerged in these years. LBSs can be applied in a variety of contexts, such as health, entertainment, and personal life. The location based data that contains significant personal information is released for analysing and mining. The privacy information of users can be attacked from the published data. In this paper, we investigate the problem of privacy-preservation of density distribution on mobility data. Different from adding noises into the original data for privacy protection, we devise the Generative Adversarial Networks (GANs) to train the generator and discriminator for generating the privacy-preserved data. We conduct extensive experiments on two real world mobile datasets. It is demonstrated that our method outperforms the differential privacy approach in both data utility and attack error.

A Survey on Privacy Preservation in Location-Based Mobile Business

2021 ◽  
Vol 13 (1) ◽  
pp. 20-39
Author(s):  
Ahmed Aloui ◽  
Okba Kazar
Keyword(s):  
Privacy Protection ◽  
Privacy Preservation ◽  
Service Providers ◽  
Personal Information ◽  
Third Party ◽  
Mobile Business ◽  
Protection Measures ◽  
System Architectures ◽  
Protection Goals ◽  
Privacy Issues

In mobile business (m-business), a client sends its exact locations to service providers. This data may involve sensitive and private personal information. As a result, misuse of location information by the third party location servers creating privacy issues for clients. This paper provides an overview of the privacy protection techniques currently applied by location-based mobile business. The authors first identify different system architectures and different protection goals. Second, this article provides an overview of the basic principles and mechanisms that exist to protect these privacy goals. In a third step, the authors provide existing privacy protection measures.

scholarly journals InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models

2020 ◽  
Vol 34 (04) ◽  
pp. 4377-4384
Author(s):  
Ameya Joshi ◽  
Minsu Cho ◽  
Viraj Shah ◽  
Balaji Pokuri ◽  
Soumik Sarkar ◽  
...  
Keyword(s):  
Image Data ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Complex Data ◽  
Challenging Problem ◽  
Two Phase ◽  
Adversarial Networks ◽  
Generative Modeling ◽  
Lack Of Control ◽  
Real World Problems

Generative Adversarial Networks (GANs), while widely successful in modeling complex data distributions, have not yet been sufficiently leveraged in scientific computing and design. Reasons for this include the lack of flexibility of GANs to represent discrete-valued image data, as well as the lack of control over physical properties of generated samples. We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. We evaluate our approach on several synthetic and real world problems: navigating manifolds of geometric shapes with desired sizes; generation of binary two-phase materials; and the (challenging) problem of generating multi-orientation polycrystalline microstructures.

scholarly journals Motion Detection to Preserve Personal Privacy from Surveillance Data using Contrary Motion

2020 ◽  
Vol 8 (6) ◽  
pp. 3892-3895
Keyword(s):  
Internet Of Things ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Smart Cities ◽  
Value Added ◽  
The Internet ◽  
Sufficient Information ◽  
Personal Privacy ◽  
Privacy And Security ◽  
The Internet Of Things

Internet of Things network today naturally is one of the huge quantities of devices from sensors linked through the communication framework to give value added service to the society and mankind. That allows equipment to be connected at anytime with anything rather using network and service. By 2020 there will be 50 to 100 billion devices connected to Internet and will generate heavy data that is to be analyzed for knowledge mining is a forecast. The data collected from individual devices of IoT is not going to give sufficient information to perform any type of analysis like disaster management, sentiment analysis, and smart cities and on surveillance. Privacy and Security related research increasing from last few years. IoT generated data is very huge, and the existing mechanisms like k- anonymity, l-diversity and differential privacy were not able to address these personal privacy issues because the Internet of Things Era is more vulnerable than the Internet Era [10][20]. To solve the personal privacy related problems researchers and IT professionals have to pay more attention to derive policies and to address the key issues of personal privacy preservation, so the utility and trade off will be increased to the Internet of Things applications. Personal Privacy Preserving Data Publication (PPPDP) is the area where the problems are identified and fixed in this IoT Era to ensure better personal privacy.

scholarly journals Cross Data Set Generalization of Ultrasound Image Augmentation using Representation Learning: A Case Study

2021 ◽  
Vol 7 (2) ◽  
pp. 755-758
Author(s):  
Daniel Wulff ◽  
Mohamad Mehdi ◽  
Floris Ernst ◽  
Jannis Hagenah
Keyword(s):  
Data Augmentation ◽  
Ultrasound Image ◽  
Image Data ◽  
Representation Learning ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Imaging Data ◽  
Data Set ◽  
Adversarial Networks ◽  
Classical Image

Abstract Data augmentation is a common method to make deep learning assessible on limited data sets. However, classical image augmentation methods result in highly unrealistic images on ultrasound data. Another approach is to utilize learning-based augmentation methods, e.g. based on variational autoencoders or generative adversarial networks. However, a large amount of data is necessary to train these models, which is typically not available in scenarios where data augmentation is needed. One solution for this problem could be a transfer of augmentation models between different medical imaging data sets. In this work, we present a qualitative study of the cross data set generalization performance of different learning-based augmentation methods for ultrasound image data. We could show that knowledge transfer is possible in ultrasound image augmentation and that the augmentation partially results in semantically meaningful transfers of structures, e.g. vessels, across domains.

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