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<p>A private data query system (PDQS) enables data consumers to access privately owned data while compensating data owners for their privacy loss. The two main tasks of a PDQS include procurement, i.e., collecting data from multiple data owners with an appropriate pricing scheme, and querying, i.e., aggregating the collected dataset for a query output while preserving data owners’ privacy. Existing PDQS are designed for unconditional queries over single-attribute data. In this paper, we design PrivCQ, a new PDQS that supports conditional queries over multi-dimensional data. To accommodate heterogeneous attribute-level privacy preferences, we introduce a new privacy concept, multi-dimensional personalised local differential privacy (m-PLDP), which specifies privacy requirements across multiple sensitive attributes for each data owner. For procurement, we propose total purchased privacy maximisation (TPPM), a principle linking query accuracy to m-PLDP. For query, we propose two techniques, attribute fusion and aggregation conditioning, to process conditional queries over multi-dimensional sensitive data. We design three query mechanisms that achieve m-PLDP under different paradigms and empirically validate them on three real-world datasets.</p>

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PrivCQ: Trading multi-dimensional conditional queries under personalised local differential privacy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

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  • Mengxiao Zhang,
  • Weidong Li,
  • Yiping Liu,
  • Bakh Khoussainov,
  • Jiamou Liu

摘要

A private data query system (PDQS) enables data consumers to access privately owned data while compensating data owners for their privacy loss. The two main tasks of a PDQS include procurement, i.e., collecting data from multiple data owners with an appropriate pricing scheme, and querying, i.e., aggregating the collected dataset for a query output while preserving data owners’ privacy. Existing PDQS are designed for unconditional queries over single-attribute data. In this paper, we design PrivCQ, a new PDQS that supports conditional queries over multi-dimensional data. To accommodate heterogeneous attribute-level privacy preferences, we introduce a new privacy concept, multi-dimensional personalised local differential privacy (m-PLDP), which specifies privacy requirements across multiple sensitive attributes for each data owner. For procurement, we propose total purchased privacy maximisation (TPPM), a principle linking query accuracy to m-PLDP. For query, we propose two techniques, attribute fusion and aggregation conditioning, to process conditional queries over multi-dimensional sensitive data. We design three query mechanisms that achieve m-PLDP under different paradigms and empirically validate them on three real-world datasets.