Privacy-preserving computing services for encrypted personal data through streams over distributed ledgers
The growing adoption of wearables is driving the demand for personalized services that leverage unprocessed data, such as biometric and health information, to enhance user experiences and support through software applications. However, several existing use cases involving this information still prio...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/67723 |
| Acceso en línea: | http://hdl.handle.net/10017/67723 https://dx.doi.org/10.1007/s44227-024-00038-9 |
| Access Level: | acceso abierto |
| Palabra clave: | Data streams DLT Encrypted data IoT Privacy-preserving data sharing Informática Computer science |
| Sumario: | The growing adoption of wearables is driving the demand for personalized services that leverage unprocessed data, such as biometric and health information, to enhance user experiences and support through software applications. However, several existing use cases involving this information still prioritize traditional schemes, neglecting user privacy. Consequently, the transparency of data transmission paths and the potential for tampering remain ambiguous when users share data with service providers. In this paper, we propose the application of an Internet of Things device-focused distributed ledger as an underlying layer for the transmission of encrypted data using streams. Moreover, our proposal enables data recording for future events and the implementation of multi-subscriber models, allowing client information to be shared securely with different service providers. Through simulation experiments conducted on constrained devices, we demonstrate that our proposed framework efficiently transmits large ciphertexts through streams on a distributed ledger, overcoming the inherent limitations of such networks when dealing with substantial data volumes. Ultimately, the performance metrics presented prove that the proposed model is suitable for real-world applications requiring continuous data collection by wearables and subsequent transmission to service providers. |
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