SS-PCDC: Secret Sharing-Based Private Collaborative Data Cleaning in Cloud-Assisted Setting
Ziyu Niu, Ye Su, Yuxia Li, Hao Wang, Tingting Pang · Mathematics · 2026
The rapid growth of sensitive labeled data in healthcare, finance, user profiling, and commercial databases has created increasing demand for privacy-preserving collaborative data validation across different organizations. Following prior cryptographic studies, this paper uses private collaborative data cleaning (PCDC) to refer to a specific label-conflict detection task rather than general-purpose data cleaning: the goal is to identify records for which the identifiers match but the associated labels are inconsistent, without revealing the remaining private records. Existing PCDC protocols are mainly designed for direct two-party settings where data owners must remain online and participate in the main secure computation. To reduce this online burden, we propose SS-PCDC, a secret sharing-based PCDC framework in a cloud-assisted setting. Clients locally preprocess and secret-share their labeled datasets with two non-colluding cloud servers, which perform element matching, label consistency checking, and conflict detection over secret shares. Hash-based binning is used to reduce unnecessary secure comparisons. We instantiate the framework with two concrete protocols based on arithmetic secret sharing and Boolean secret sharing, respectively. We further extend exact PCDC to threshold-based fuzzy label conflict detection and propose SS-FPCDC, which reports a matched record as conflicting when the Hamming distance between its labels exceeds a public threshold. Security analyses show that the proposed protocols securely realize their corresponding ideal functionalities against a static semi-honest adversary corrupting at most one cloud server. Experimental results demonstrate the efficiency and scalability of SS-PCDC in its intended cloud-assisted setting, particularly for large datasets and longer labels.