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Privacy preserving data sharing and analysis for edge-based architectures

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Abstract

In this paper, we present a framework for privacy preserving collaborative data analysis among multiple data providers acting as edge of a cloud environment. The proposed framework computes the best trade-off among privacy and result accuracy, based on the privacy requirements of data providers and the specific requested analysis algorithm. Though the presented model is general and can be applied to different environments, this work is motivated by the need of sharing information related to Cyber Threats (CTI). The presented framework is independent from the number of data providers, used data format, privacy requirement and analysis operations. The model is based on the concepts of trade-off score between accuracy and privacy, which also considers measures for privacy requirement such as differential privacy, l-diversity and k-anonymity. Together with the model, the paper discusses the framework implementation and presents results to show the effectiveness and viability of the proposed approach.

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Notes

  1. https://www.mitre.org/about.

  2. http://docs.oasis-open.org/cti/stix/v2.0/stix-v2.0-part2-stix-objects.html

  3. The other privacy-preserving techniques in this category, such as data swapping, are not common approaches. Therefore, we do not consider them in this study.

  4. The process is iteratively repeated for 4 times to fulfill the 5 privacy levels’ requirement.

  5. https://www.oasis-open.org/.

  6. https://www.mitre.org/.

  7. https://stixproject.github.io/.

  8. https://cve.mitre.org/.

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Acknowledgements

This work has been partially supported by the H2020 EU-funded projects C3ISP (GA #700294), SIFIS-Home (GA #952652) and the ECSEL project SECREDAS (#783119).

Funding

Authors have received funding only from Public International research agencies.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands

    Mina Sheikhalishahi

  2. Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Andrea Saracino & Fabio Martinelli

  3. Security Forge srl, Pisa, Italy

    Antonio La Marra

Authors
  1. Mina Sheikhalishahi
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  2. Andrea Saracino
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  3. Fabio Martinelli
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  4. Antonio La Marra
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Correspondence to Andrea Saracino.

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Sheikhalishahi, M., Saracino, A., Martinelli, F. et al. Privacy preserving data sharing and analysis for edge-based architectures. Int. J. Inf. Secur. 21, 79–101 (2022). https://doi.org/10.1007/s10207-021-00542-x

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