Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges

2019 ◽  
Vol 140-141 ◽  
pp. 38-60 ◽  
Author(s):  
Josep Domingo-Ferrer ◽  
Oriol Farràs ◽  
Jordi Ribes-González ◽  
David Sánchez
IEEE Network ◽  
2018 ◽  
Vol 32 (3) ◽  
pp. 7-13 ◽  
Author(s):  
Kaiping Xue ◽  
Jianan Hong ◽  
Yongjin Ma ◽  
David S. L. Wei ◽  
Peilin Hong ◽  
...  

2014 ◽  
Vol 631-632 ◽  
pp. 897-901
Author(s):  
Xian Yong Meng ◽  
Zhong Chen ◽  
Xiang Yu Meng

In this paper, a novel proxy re-encryption (PRE) scheme with keyword search is proposed, where only the ciphertext containing the keyword set by the delegator can be transformed by the semi-trusted proxy and then decrypted by delegatee. In the proposed scheme, the semi-trusted proxy can convert the ciphertext encrypted under the delegator’s public key into the ciphertext encrypted under the delegatee’s public key. In addition, only the delegatee’s email gateway with a trapdoor can test whether or not a given cipheretext containing some keyword, but can learn nothing else about the sensitive data of email. We proposed an identity-based proxy re-encryption with keyword search scheme, where the delegator and the delegatee extract keys from a trusted party called the key generator center (KGC), who generates public-private key pair for delegator and delegatee based on their identities. Meanwhile, the identity-based proxy re-encryption with keyword search scheme satisfies the properties of PRE including unidirectionality, multi-use and transparency. Additionally, the proposed scheme is efficient in terms of both computation and communication, and can realize security privacy preserving in cloud computing environments.


2012 ◽  
Vol 35 (11) ◽  
pp. 2215 ◽  
Author(s):  
Fang-Quan CHENG ◽  
Zhi-Yong PENG ◽  
Wei SONG ◽  
Shu-Lin WANG ◽  
Yi-Hui CUI

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Qi Dou ◽  
Tiffany Y. So ◽  
Meirui Jiang ◽  
Quande Liu ◽  
Varut Vardhanabhuti ◽  
...  

AbstractData privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.


Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1367
Author(s):  
Raghida El El Saj ◽  
Ehsan Sedgh Sedgh Gooya ◽  
Ayman Alfalou ◽  
Mohamad Khalil

Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.


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