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Efficient Collaborative Data Cleaning Using Private Set Intersection and Encoding for Unbalanced Datasets
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DOI：10.1109/TIFS.2025.3594871.png)
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Abstract
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Data cleaning improves quality and consistency by detecting, localizing, and repairing “dirty” data without compromising sensitive information. Collaborative Data Cleaning employs a distributed model to avoid single points of failure and trust issues in centralized systems, although it incurs additional communication overhead. Blass et al. (S&P’23) were the first to implement CDC via balanced Private Set Intersection (PSI). Unbalanced PSI (e.g., uPSI-CA, USENIX’23) does not address the localization of intersections within datasets and thus cannot be directly applied to CDC. uPSI-based data cleaning remains largely unexplored. In this paper, we propose an efficient CDC scheme for unbalanced datasets, named uECDC. uECDC employs oblivious key-value stores for slice matching, achieving: 1) a reduction of 18% $\thicksim ~85$ % in offline phase runtime, and 2) a reduction of 8% $\thicksim ~43$ % in online phase runtime (under large-scale data settings on the server side), when compared to the slice-linking approach of uPSI-CA. Moreover, we encode server-side data for fast localization of intersection data in unbalanced settings. Under the semi-honest adversary model, uECDC is provably secure. Implementation in Python and C++ demonstrates that uECDC is practically feasible.
Keywords:
Collaborative data cleaning
private set intersection
oblivious key-value stores
unbalanced dataset
privacy protection
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Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security - CCS '13
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Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security - CCS '13IF0
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