A Privacy Preserving Repository for Data Integration across Data Sharing Services

2008 ◽  
Vol 1 (3) ◽  
pp. 130-140 ◽  
Author(s):  
Stephen S. Yau ◽  
Yin Yin
Keyword(s):  
Data Integration ◽  
Data Sharing ◽  
Privacy Preserving

scholarly journals SPChain: Blockchain-based medical data sharing and privacy-preserving eHealth system

2021 ◽  
Vol 58 (4) ◽  
pp. 102604
Author(s):  
Renpeng Zou ◽  
Xixiang Lv ◽  
Jingsong Zhao
Keyword(s):  
Data Sharing ◽  
Privacy Preserving ◽  
Medical Data

Traceable and Privacy-Preserving Non-Interactive Data Sharing in Mobile Crowdsensing

2021 ◽  
Author(s):  
Fuyuan Song ◽  
Zheng Qin ◽  
Jinwen Liang ◽  
Pulei Xiong ◽  
Xiaodong Lin
Keyword(s):  
Data Sharing ◽  
Privacy Preserving ◽  
Mobile Crowdsensing ◽  
Interactive Data

scholarly journals Efficient and Privacy-Preserving Medical Data Sharing in Internet of Things With Limited Computing Power

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 28019-28027 ◽  
Author(s):  
Dong Zheng ◽  
Axin Wu ◽  
Yinghui Zhang ◽  
Qinglan Zhao
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Privacy Preserving ◽  
Medical Data ◽  
Computing Power

Secure Channel for Sharing Datasets by using Privacy-Preserving Integration Method

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
G Sriman Narayana ◽  
Kuruva Arjun Kumar
Keyword(s):  
Data Integration ◽  
Integration Method ◽  
Privacy Preserving ◽  
The Other ◽  
Third Party ◽  
Traffic Data ◽  
Structure Preserving ◽  
Efficient Approach ◽  
The Third ◽  
Secure Channel

In privacy-enhancing technology, it has been inevitably challenging to strike a maintain balance between privacy, efficiency and usability (utility). We propose a highly practical and efficient approach for privacy-preserving integration and sharing of datasets among a group of participants. At the heart of our solution is a new interactive protocol, Secure Channel. Through Secure Channel, each participant is able to randomize their datasets via an independent and untrusted third party, such that the resulting dataset can be merged with other randomized datasets contributed by other participants group in a privacy-preserving manner. Our process does not require any public or key sharing between participants in order to integrate different datasets. This, in turn, leads to a user can understand and use easily and scalable solution. Moreover, the accuracy of a randomized dataset which are returned by the third party can be securely verified by the other participant of group. We further demonstrate Secure Channel’s general utilities, using it to construct a structure preserving data integration protocol. This is mainly useful for, good quality integration of network traffic data.

Privacy Preserving Integration of Health Care Data

2012 ◽  
pp. 94-107 ◽  
Author(s):  
Xiaoyun He ◽  
Jaideep Vaidya ◽  
Basit Shafiq ◽  
Nabil Adam ◽  
Tom White
Keyword(s):  
Health Care ◽  
Data Integration ◽  
Privacy Preserving ◽  
Cross Linking ◽  
Health Care Information ◽  
Multiple Sources ◽  
Rule Based ◽  
Related Research ◽  
Health Care Data ◽  
Care Information

For health care related research studies the medical records of patients may need to be retrieved from multiple sites with different regulations on the disclosure of health information. Given the sensitive nature of health care information, privacy is a major concern when patients’ health care data is used for research purposes. In this paper, the authors propose approaches for integration and querying of health care data from multiple sources in a secure and privacy preserving manner. In particular, the first approach ensures secure data integration based on unique identifiers, and the second one considers data integration based on quasi identifiers, for which a rule-based framework is proposed for cross-linking data records, including secure character matching.

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