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Secure personal data sharing for simultaneous, parallel or sequential processing service: Autonomously and controllably
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AI
DOI：10.1016/j.future.2025.107879.png)
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Abstract
En 中文
Personal data, as an important category of data elements of a trusted data circulation, needs to be shared to others in one-to-one mode or one-to-many mode with simultaneously, parallelly or sequentially to meet all kinds of complex business scenarios. Actually, today's application asks more personalized content and more often than ever before, it increasingly highlights the need for data owners to have privacy rights such as the right to be informed and the right to erasure. However, existing personal data sharing schemes mainly deal with one-to-one mode and some of them have made breakthroughs in one-to-many mode, they still do not support complex scenarios such as simultaneously operating, parallelly executing or sequentially processing in a data privacy protection way. Therefore, this paper first encapsulates personal data into data capsules and designs three types of access tasks based on the requirements of complex scenarios to achieve secure collaborative sharing, secure multi-copies sharing, and secure ordered sharing. Specifically, the scheme embeds fine-grained authorization mechanisms within the data capsules and combines them with access task tokens that support autonomous control, achieving personal data sharing autonomy, including selective sharing, informed consent authorization, and permission revocation. Finally, the security of the proposed scheme is proven through formal and informal security proofs. The results of performance analysis show that the proposed scheme has better computational and storage efficiency while supporting the data sharing needs of complex scenarios.
Keywords:
Data sharing
Complex scenarios
Data capsule
Access task
Personal data sharing autonomy
Journal
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Citations:
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