On measuring the privacy of anonymized data in multiparty network data sharing

2013 ◽  
Vol 10 (5) ◽  
pp. 120-127 ◽  
Author(s):  
Chen Xiaoyun ◽  
Su Yujie ◽  
Tang Xiaosheng ◽  
Huang Xiaohong ◽  
Ma Yan
Keyword(s):  
Data Sharing ◽  
Network Data ◽  
Anonymized Data

scholarly journals Research on Distributed Data Sharing System based on Internet of Things and Blockchain

2021 ◽  
Vol 9 (3) ◽  
pp. 239-254
Author(s):  
Enchang Sun ◽  
Kang Meng ◽  
Ruizhe Yang ◽  
Yanhua Zhang ◽  
Meng Li
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Data Storage ◽  
Network Architecture ◽  
Data Privacy ◽  
Distributed Storage ◽  
Global Network ◽  
Local Network ◽  
Network Data ◽  
Distributed Data

Abstract Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

Ad Hoc network data sharing mechanism based on service evaluation management

2011 ◽  
Vol 30 (12) ◽  
pp. 3164-3167
Author(s):  
Xue-mei ZHOU ◽  
Duo PAN ◽  
Bo-hui WANG
Keyword(s):  
Data Sharing ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Service Evaluation ◽  
Network Data

scholarly journals Burning the Haystack to Save the Needle: Dimension Reduction and Privacy in Text and Network Data

2019 ◽  
Author(s):  
Alexander Murray-Watters
Keyword(s):  
Social Networks ◽  
Dimension Reduction ◽  
Predictive Modeling ◽  
Reduction Technique ◽  
Distance Measures ◽  
Network Data ◽  
Data Types ◽  
Reduction Procedure ◽  
Anonymized Data

This poster presents results from applying a new dimension reduction technique (UMAP) to a wide variety of data types, ranging from online text to social networks, for the purpose of creating useful, but anonymized, data. As the dimension reduction procedure produces meaningful distances and supports arbitrary distance measures, it can be applied to a variety of problems, and produces data that is useful for both visualization and predictive modeling. Included is a description of the dimension reduction procedure, the results of its application, and a discussion of planned future use.

scholarly journals Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive

2014 ◽  
Vol 7 (1) ◽  
pp. 147-152 ◽  
Author(s):  
Jayashree Kalpathy-Cramer ◽  
John Blake Freymann ◽  
Justin Stephen Kirby ◽  
Paul Eugene Kinahan ◽  
Fred William Prior
Keyword(s):  
Data Sharing ◽  
Cancer Imaging ◽  
Quantitative Imaging ◽  
Network Data

scholarly journals Refacing: Reconstructing Anonymized Facial Features Using Gans

2018 ◽  
Author(s):  
David Abramian ◽  
Anders Eklund
Keyword(s):  
Deep Learning ◽  
Data Sharing ◽  
Medical Images ◽  
Facial Features ◽  
The Novel ◽  
Mr Images ◽  
Essential Step ◽  
Image Translation ◽  
Recent Developments ◽  
Anonymized Data

ABSTRACTAnonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing. However, recent developments in deep learning may raise the bar on the amount of distortion that needs to be applied to guarantee anonymity. To test such possibilities, we have applied the novel CycleGAN unsupervised image-to-image translation framework on sagittal slices of T1 MR images, in order to reconstruct facial features from anonymized data. We applied the CycleGAN framework on both face-blurred and face-removed images. Our results show that face blurring may not provide adequate protection against malicious attempts at identifying the subjects, while face removal provides more robust anonymization, but is still partially reversible.

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