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Outsourcing collaboration analysis of multiparty privacy data using the improved Yannakakis

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Abstract

Many organizations are producing or collecting private data in fields such as medical research and government regulation. Due to current privacy protection, laws and regulations, commercial competition, and other issues, these institutions cannot directly share their data. Collaborative analysis of private data from multiple institutions will benefit each institution and create profits together. Therefore, we propose a Yannakakis-based multiparty outsourcing collaboration analysis scheme. It enables organizations to collaboratively analyze private data from multiple organizations according to their needs while ensuring that private data are not leaked to each other. Our scheme is based on the improved Yannakakis algorithm to build a series of query components, such as Semi-join, Join, Order-by, etc. We also optimized the join operation. By confusing the input tuples and protecting their authenticity through annotations, the join operation can be directly joined through the hash value without disclosing the join results. Through this series of configurations, we can execute a query with \( O(\textrm{IN}+\textrm{OUT}) \) runtime and communication, where \( \textrm{IN} \) is the total number of tuples in the input relationship, and \( \textrm{OUT} \) is the output size. We have carried out a series of comparative experiments, and the results show that our system is 1.3X–7.4X faster than the baseline.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The academic staffs of School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, China conducted this study. The authors would like to express their gratitude to the institute for its assistance with various resources as well as its assistance during the research process. The authors appreciate the reviewers for their helpful suggestions.

Funding

This work is supported by the Opening Project of Intelligent Policing Key Laboratory of Sichuan Province, No.ZNJW2022KFZD002.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Cyberspace Security Monitoring and Governance, Chongqing University of Post and Telecommunications, Chongqing, 400065, China

    Zigang Chen, Zhenjiang Zhang & Haihua Zhu

  2. Intelligent Policing Key Laboratory of Sichuan Province, Sichuan Police College, Luzhou, 646000, Sichuan, China

    Zigang Chen, Tao Leng & Haihua Zhu

  3. Department of Computer Science and Engineering, Santa Clara University, Santa Clara, CA, 95053, USA

    Yuhong Liu

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  1. Zigang Chen
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Contributions

Zigang Chen performed conceptualization, methodology, and software. Zhenjiang Zhang presented formal analysis, data curation, writing—original draft, and writing—review & editing. Haihua Zhu and Tao Leng carried out visualization, investigation, and data curation. Yuhong Liu analyzed conceptualization and methodology. All authors read and approved the final manuscript.

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Correspondence to Tao Leng or Haihua Zhu.

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Chen, Z., Zhang, Z., Leng, T. et al. Outsourcing collaboration analysis of multiparty privacy data using the improved Yannakakis. J Supercomput 81, 497 (2025). https://doi.org/10.1007/s11227-025-06994-5

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