Privacy Preserving Data Access Research Articles

Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare - a proof of concept study for the prediction of coronary artery calcification scores.

The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could support that is federated machine learning (FL) since it allows for privacy preserving data access. For this proof of concept, a prediction model for coronary artery calcification scores (CACS) has been applied. The FL was trained based on the data in the different institutions, while the centralized machine learning model was trained on one allocation of data. Both algorithms predict patients with risk scores ≥5 based on age, biological sex, waist circumference, dyslipidemia and HbA1c. The centralized model yields a sensitivity of c. 66% and a specificity of c. 70%. The FL slightly outperforms that with a sensitivity of 67% while slightly underperforming it with a specificity of 69%. It could be demonstrated that CACS prediction is feasible via both, a centralized and an FL approach, and that both show very comparable accuracy. In order to increase accuracy, additional and a higher volume of patient data is required and for that FL is utterly necessary. The developed "CACulator" serves as proof of concept, is available as research tool and shall support future research to facilitate AI implementation.

Blockchain-Enabled and Data-Driven Smart Healthcare Solution for Secure and Privacy-Preserving Data Access

The major advances in body-mounted sensors and wireless technologies have been revolutionizing the healthcare industry, where patient’s conditions can be remotely monitored by medical staff. Such a model is gaining broad support due to its economic and social advantages. However, the wealth of sensor measurements pose major technical challenges on where to store the collected data, how to ensure its integrity, who control access permissions, and how to enable secure interaction between patients and medical facilities and professionals. This article aspires to provide a holistic solution based on blockchain technology. Our solution puts the patient in charge for granting and revoking access permissions and makes it easy for healthcare organizations and providers to meet privacy regulations. The sensor data are to reside on cloud storage, while access control and session logs are maintained on blockchain. In addition, a novel data-driven authentication and secure communication protocol is proposed to mitigate the risk of fraud and identity theft. In order to enforce such a protocol, all interactions between the cloud and patients and healthcare providers are regulated through smart contracts. The security properties of our solution are analyzed using AVISPA; it is also shown to be computationally efficient.

Federated learning for privacy-preserving data access

Federated learning is a pioneering privacy-preserving data technology and also a new machine learning model trained on distributed data sets. Companies collect huge amounts of historic and real-time data to drive their business and collaborate with other organisations. However, data privacy is becoming increasingly important because of regulations (e.g. EU GDPR) and the need to protect their sensitive and personal data. Companies need to manage data access: firstly within their organizations (so they can control staff access), and secondly protecting raw data when collaborating with third parties. What is more, companies are increasingly looking to ‘monetize’ the data they’ve collected. However, under new legislations, utilising data by different organization is becoming increasingly difficult (Yu, 2016). Federated learning pioneered by Google is the emerging privacy- preserving data technology and also a new class of distributed machine learning models. This paper discusses federated learning as a solution for privacy-preserving data access and distributed machine learning applied to distributed data sets. It also presents a privacy-preserving federated learning infrastructure.

Federated Learning for Privacy-Preserving Data Access

Federated learning is a pioneering privacy-preserving data technology and also a new machine learning model trained on distributed data sets. Companies collect huge amounts of historic and real-time data to drive their business and collaborate with other organisations. However, data privacy is becoming increasingly important because of regulations (e.g. EU GDPR) and the need to protect their sensitive and personal data. Companies need to manage data access: firstly within their organizations (so they can control staff access), and secondly protecting raw data when collaborating with third parties. What is more, companies are increasingly looking to ‘monetize’ the data they’ve collected. However, under new legislations, utilising data by different organization is becoming increasingly difficult (Yu, 2016). Federated learning pioneered by Google is the emerging privacy- preserving data technology and also a new class of distributed machine learning models. This paper discusses federated learning as a solution for privacy-preserving data access and distributed machine learning applied to distributed data sets. It also presents a privacy-preserving federated learning infrastructure.

Achieving Secure, Flexible, Effective and Privacy Preserving Data Access Control in Cloud Computing

Achieving Secure, Flexible, Effective and Privacy Preserving Data Access Control in Cloud Computing

Secure Multi-Authority Data Access Control Scheme in Cloud Storage System Based on Attribute-Based Signcryption

Nowadays, secure data access control has become one of the major concerns in a cloud storage system. As a logical combination of attribute-based encryption and attribute-based signature, attribute-based signcryption (ABSC) can provide confidentiality and an anonymous authentication for sensitive data and is more efficient than traditional “encrypt-then-sign” or “sign-then-encrypt” strategies. Thus, ABSC is suitable for fine-grained access control in a semi-trusted cloud environment and is gaining more and more attention in recent years. However, in many previous ABSC schemes, user’s sensitive attributes can be disclosed to the authority, and only a single authority that is responsible for attribute management and key generation exists in the system. In this paper, we propose PMDAC-ABSC, a novel privacy-preserving data access control scheme based on Ciphertext-Policy ABSC, to provide a fine-grained control measure and attribute privacy protection simultaneously in a multi-authority cloud storage system. The attributes of both the signcryptor and the designcryptor can be protected to be known by the authorities and cloud server. Furthermore, the decryption overhead for user is significantly reduced by outsourcing the undesirable bilinear pairing operations to the cloud server without degrading the attribute privacy. The proposed scheme is proven to be secure in the standard model and has the ability to provide confidentiality, unforgeability, anonymous authentication, and public verifiability. The security analysis, asymptotic complexity comparison, and implementation results indicate that our construction can balance the security goals with practical efficiency in computation.

English

Cloud computing is rising as a prevailing data interactive model to take in users’ data which has been remotely stored in an online cloud server. On-demand cloud applications is offered by the cloud services without any local infrastructure limitations. Multiple users may wish to access and share another user's authorised data fields to achieve successful benefits. whenever user request another users for information sharing, Access request itself may uncover the client's security regardless of whether it can obtain the data access permissions or not. The option of granting/revoking data access and ranking at the admin level for the number of times the particular file has been requested is not present in the existing system .In a Proposed System Ranking Based shared authority privacypreserving authentication protocol (RSAPA) address the above privacy issue for cloud storage. And provide ranking at the Admin level .In the RSAPA,1) shared access authority is achieved by the anonymous access request matching mechanism, without out revealing the user’s privacy for a data demand 2) user can access his own data fields by attribute based access control 3) Data sharing between the different users is achieved by proxy re-encryption in cloud server In the mean time, universal compose capability (UC) model is built to demonstrate that the SAPA hypothetically has the outline accuracy .It demonstrates that the proposed protocol acknowledging privacy-preserving data access authority sharing, is keen for multi-user collaborative cloud applications. In our model, privacy is achieved by encrypting the data it can prevent the unapproved access. Also, we are going to raise the security level of the owner of the data and confidentiality of the information by giving access to the users

Preserving Privacy by Enhancing Security inCloud

Cloud computing is emerging as a prevalent data interactive paradigm to realize users’ data remotely stored in an online cloud server. Cloud services provide more benefits for the users to enjoy the on-demand cloud applications without considering the local limitations. When accessing the data, many users may be in a collaborative relationship, and thus sharing of is significant to achieve productive benefits. The present solutions for security focuses on the authentication to realize that a user’s privative data cannot be accessed if it is found to be unauthorized, but it eliminates a subtle privacy issue during a user challenging the cloud server to request other users when the data being shared. The challenge is the request for accessing reveals the user’s privacy no matter whether it has got the data access permissions. The distributed privacy-preserving authentication protocol (SAPA) which is authority based to address above privacy issue for cloud storage. shared authority based privacy-preserving authentication protocol (SAPA) to address above privacy issue for cloud storage. In the SAPA, 1) shared access is achieved by anonymous access request matching mechanism with security and privacy considerations 2) attribute based access control is adopted to realize that the user can only access its own data fields; 3) proxy re-encryption is applied by the cloud server to provide data sharing among the many users. Meanwhile, universal compos ability (UC) model is established to prove that the SAPA theoretically has the correct design. This clearly shows that the proposed protocol realizing privacy-preserving data access authority sharing is impressive for multiple users collaborative cloud applications.