Intelligent conditional collaborative private data sharing

Bianchi, G; Dargahi, T; Caponi, A; Conti, M

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.

Citation

Bianchi, G., Dargahi, T., Caponi, A., & Conti, M. (2019). Intelligent conditional collaborative private data sharing. Future Generation Computer Systems, 96(Jul 19), 1-10. https://doi.org/10.1016/j.future.2019.01.001

• Journal Article Type: Article
• Acceptance Date: Jan 1, 2019
• Online Publication Date: Jan 5, 2019
• Publication Date: Jan 5, 2019
• Deposit Date: Jan 18, 2019
• Publicly Available Date: Jan 5, 2020
• Journal: Future Generation Computer Systems
• Print ISSN: 0167-739X
• Publisher: Elsevier
• Volume: 96
• Issue: Jul 19
• Pages: 1-10
• DOI: https://doi.org/10.1016/j.future.2019.01.001
• Publisher URL: https://doi.org/10.1016/j.future.2019.01.001
• Related Public URLs: https://www.journals.elsevier.com/future-generation-computer-systems