scholarly journals An Index Structure for Private Data Outsourcing

2011 ◽  
pp. 247-254 ◽  
Author(s):  
Aaron Steele ◽  
Keith B. Frikken
Keyword(s):  
Index Structure ◽  
Data Outsourcing ◽  
Private Data

Supporting Concurrency in Private Data Outsourcing

2011 ◽  
pp. 648-664 ◽  
Author(s):  
Sabrina De Capitani di Vimercati ◽  
Sara Foresti ◽  
Stefano Paraboschi ◽  
Gerardo Pelosi ◽  
Pierangela Samarati
Keyword(s):  
Data Outsourcing ◽  
Private Data

Privacy Issues and Protection in Secure Data Outsourcing

2014 ◽  
Vol 69 (6) ◽  
Author(s):  
Touraj Khodadadi ◽  
A. K. M. Muzahidul Islam ◽  
Sabariah Baharun ◽  
Shozo Komaki
Keyword(s):  
Access Control ◽  
Privacy Protection ◽  
Control Methods ◽  
Data Outsourcing ◽  
Database Performance ◽  
Private Data ◽  
Database Encryption ◽  
Secure Data ◽  
Database Content ◽  
Privacy Issues

Utilizing database encryption to safeguard data in several conditions where access control is not sufficient is unavoidable. Database encryption offers an extra layer of security to traditional access control methods. It stops users that are unauthorized, such as hackers breaking into a system, and observing private data. Consequently, data is safe even when the database is stolen or attacked. Nevertheless, the process of data decryption and encryption causes degradation in the database performance. In conditions where the entire information is kept in an encrypted format, it is not possible to choose the database content any longer. The data must be first decrypted, and as such, the unwilling and forced tradeoff occurs between the function and the security. The suitable methods to improve the function are techniques that directly deal with the data that is encrypted without having to decrypt them first. In this study, we determined privacy protection and issues that each organization should consider when it decides to outsource own data.  

Privacy-preserving Collaborative Training for Medical Image Analysis Based on Multi-Blockchain

2020 ◽  
Vol 23 ◽  
Author(s):  
Wanlu Zhang ◽  
Qigang Wang ◽  
Mei Li
Keyword(s):  
Medical Image ◽  
Data Privacy ◽  
Medical Image Analysis ◽  
Auxiliary Information ◽  
Training Process ◽  
Private Data ◽  
Medical Institutions ◽  
Model Training ◽  
Collaborative Training ◽  
Similar Task

Background: As artificial intelligence and big data analysis develop rapidly, data privacy, especially patient medical data privacy, is getting more and more attention. Objective: To strengthen the protection of private data while ensuring the model training process, this article introduces a multi-Blockchain-based decentralized collaborative machine learning training method for medical image analysis. In this way, researchers from different medical institutions are able to collaborate to train models without exchanging sensitive patient data. Method: Partial parameter update method is applied to prevent indirect privacy leakage during model propagation. With the peer-to-peer communication in the multi-Blockchain system, a machine learning task can leverage auxiliary information from another similar task in another Blockchain. In addition, after the collaborative training process, personalized models of different medical institutions will be trained. Results: The experimental results show that our method achieves similar performance with the centralized model-training method by collecting data sets of all participants and prevents private data leakage at the same time. Transferring auxiliary information from similar task on another Blockchain has also been proven to effectively accelerate model convergence and improve model accuracy, especially in the scenario of absence of data. Personalization training process further improves model performance. Conclusion: Our approach can effectively help researchers from different organizations to achieve collaborative training without disclosing their private data.

scholarly journals Generating Private Data Surrogates for Vision Related Tasks

2021 ◽  
Author(s):  
Ryan Webster ◽  
Julien Rabin ◽  
Loic Simon ◽  
Frederic Jurie
Keyword(s):  
Private Data
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