Intelligent conditional collaborative private data sharing

Bianchi, G, Dargahi, T ORCID: https://orcid.org/0000-0002-0908-6483, Caponi, A and Conti, M 2019, 'Intelligent conditional collaborative private data sharing' , Future Generation Computer Systems, 96 (Jul 19) , pp. 1-10.

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Abstract

With the advent of distributed systems, secure and privacy-preserving data sharing between different entities (individuals or organizations) becomes a challenging issue. There are several real-world scenarios in which different entities are willing to share their private data only under certain circumstances, such as sharing the system logs when there is indications of cyber attack in order to provide cyber threat intelligence. Therefore, over the past few years, several researchers proposed solutions for collaborative data sharing, mostly based on existing cryptographic algorithms. However, the existing approaches are not appropriate for conditional data sharing, i.e., sharing the data if and only if a pre-defined condition is satisfied due to the occurrence of an event. Moreover, in case the existing solutions are used in conditional data sharing scenarios, the shared secret will be revealed to all parties and re-keying process is necessary. In this work, in order to address the aforementioned challenges, we propose, a “conditional collaborative private data sharing” protocol based on Identity-Based Encryption and Threshold Secret Sharing schemes. In our proposed approach, the condition based on which the encrypted data will be revealed to the collaborating parties (or a central entity) could be of two types: (i) threshold, or (ii) pre-defined policy. Supported by thorough analytical and experimental analysis, we show the effectiveness and performance of our proposal.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Future Generation Computer Systems
Publisher: Elsevier
ISSN: 0167-739X
Related URLs:
Depositing User: T Dargahi
Date Deposited: 18 Jan 2019 14:05
Last Modified: 05 Jan 2020 02:30
URI: http://usir.salford.ac.uk/id/eprint/49786

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