A cloud-based IoMT data sharing scheme with conditional anonymous source authentication
- Wang, Yan-Ping, Wang, Xiao-Fen, Dai, Hong-Ning, Zhang, Xiao-Song, Su, Yu, Imran, Muhammad, Nasser, Nidal
- Authors: Wang, Yan-Ping , Wang, Xiao-Fen , Dai, Hong-Ning , Zhang, Xiao-Song , Su, Yu , Imran, Muhammad , Nasser, Nidal
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE Global Communications Conference, GLOBECOM 2022, Virtual, online, 4-8 December 2022, 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings p. 2915-2920
- Full Text: false
- Reviewed:
- Description: As a rapidly growing subset of the Internet of Thing (IoT), the cloud-based Internet of Medical Thing (IoMT) has been widely applied in remote healthcare industries, which allows the physicians to monitor patients' body parameters remotely to offer continuous and timely healthcare. These healthcare parameters usually contain sensitive information, such as heart rates, glucose levels and etc., and the exposure of them may pose serious threats to the patients' health and lives. To guarantee security and privacy, many IoMT data sharing schemes have been proposed. However, most of these schemes either exhibit a one-to-one data sharing structure or fail to protect the patients' privacy. Since the data usually needs to be shared to different physicians, patients may want to be assisted without revealing their identities. To meet these requirements in healthcare systems, we propose a multi-receiver secure healthcare data sharing scheme, in which the patients are allowed to share their IoMT data to multiple physicians simultaneously for a multidisciplinary treatment, and the conditional anonymity is achieved where data source authentication is provided without revealing the patient's identity. When the patient health condition is abnormal, the hospital can correctly and quickly trace the patient's identity and inform him/her immediately. Our scheme is formally proved to achieve multiple security properties including confidentiality, unforgeability and anonymity. Simulation results demonstrate that the proposed scheme is efficient and practical. © 2022 IEEE.
Third party data service providers can enhance patient-provider interactions : insights from a Delphi study
- Hashmi, Mustafa, McInnes, Angelique, Stranieri, Andrew, Sahama, Tony
- Authors: Hashmi, Mustafa , McInnes, Angelique , Stranieri, Andrew , Sahama, Tony
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 224-228
- Full Text:
- Reviewed:
- Description: Data sharing between financial services organisations has led to a proliferation of third party data service providers that are not parties to transactions but facilitate interactions between them by analysing, manipulating or storing data related to transactions. This has led to widespread legal, technological and sociocultural changes in that sector broadly described as Open-Banking initiatives. Third party service providers have not emerged in the healthcare sector in the same way. This study reports preliminary results of a Delphi study comprising healthcare and financial experts to explore the extent to which third party providers in healthcare is beneficial and feasible. Ensuring the quality of data service provided by third parties was seen to be a critical success factor. A causal loop model was used to describe the inter-dependent factors underpinning this factor. Further investigations to augment the model with Consumer Data Rights and validate empirically are underway. © 2022 ACM.
- Authors: Hashmi, Mustafa , McInnes, Angelique , Stranieri, Andrew , Sahama, Tony
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 224-228
- Full Text:
- Reviewed:
- Description: Data sharing between financial services organisations has led to a proliferation of third party data service providers that are not parties to transactions but facilitate interactions between them by analysing, manipulating or storing data related to transactions. This has led to widespread legal, technological and sociocultural changes in that sector broadly described as Open-Banking initiatives. Third party service providers have not emerged in the healthcare sector in the same way. This study reports preliminary results of a Delphi study comprising healthcare and financial experts to explore the extent to which third party providers in healthcare is beneficial and feasible. Ensuring the quality of data service provided by third parties was seen to be a critical success factor. A causal loop model was used to describe the inter-dependent factors underpinning this factor. Further investigations to augment the model with Consumer Data Rights and validate empirically are underway. © 2022 ACM.
Best practice data life cycle approaches for the life sciences
- Griffin, Philippa, Khadake, Jyoti, LeMay, Kate, Lewis, Suzanna, Orchard, Sandra, Pask, Andrew, Pope, Bernard, Roessner, Ute, Russell, Keith, Seemann, Torsten, Treloar, Andrew, Tyagi, Sonika, Christiansen, Jeffrey, Dayalan, Saravanan, Gladman, Simon, Hangartner, Sandra, Hayden, Helen, Ho, William, Keeble-Gagnère, Gabriel, Korhonen, Pasi, Neish, Peter, Prestes, Priscilla, Richardson, Mark, Watson-Haigh, Nathan, Wyres, Kelly, Young, Neil, Schneider, Maria
- Authors: Griffin, Philippa , Khadake, Jyoti , LeMay, Kate , Lewis, Suzanna , Orchard, Sandra , Pask, Andrew , Pope, Bernard , Roessner, Ute , Russell, Keith , Seemann, Torsten , Treloar, Andrew , Tyagi, Sonika , Christiansen, Jeffrey , Dayalan, Saravanan , Gladman, Simon , Hangartner, Sandra , Hayden, Helen , Ho, William , Keeble-Gagnère, Gabriel , Korhonen, Pasi , Neish, Peter , Prestes, Priscilla , Richardson, Mark , Watson-Haigh, Nathan , Wyres, Kelly , Young, Neil , Schneider, Maria
- Date: 2018
- Type: Text , Journal article
- Relation: F1000 Research Vol. 6, no. (2018), p. 1-28
- Full Text:
- Reviewed:
- Description: Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices. © 2018 Griffin PC et al.
- Authors: Griffin, Philippa , Khadake, Jyoti , LeMay, Kate , Lewis, Suzanna , Orchard, Sandra , Pask, Andrew , Pope, Bernard , Roessner, Ute , Russell, Keith , Seemann, Torsten , Treloar, Andrew , Tyagi, Sonika , Christiansen, Jeffrey , Dayalan, Saravanan , Gladman, Simon , Hangartner, Sandra , Hayden, Helen , Ho, William , Keeble-Gagnère, Gabriel , Korhonen, Pasi , Neish, Peter , Prestes, Priscilla , Richardson, Mark , Watson-Haigh, Nathan , Wyres, Kelly , Young, Neil , Schneider, Maria
- Date: 2018
- Type: Text , Journal article
- Relation: F1000 Research Vol. 6, no. (2018), p. 1-28
- Full Text:
- Reviewed:
- Description: Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices. © 2018 Griffin PC et al.
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