What question did this study set out to answer?

The aim is to develop a private data query system that enables conditional queries over multi-dimensional sensitive data.

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

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PrivCQ demonstrates enhanced query accuracy in multi-dimensional data while ensuring personalised local differential privacy.

The aim is to develop a private data query system that enables conditional queries over multi-dimensional sensitive data.
Designed PrivCQ for conditional queries on multi-dimensional data.
Introduced multi-dimensional personalised local differential privacy (m-PLDP).
Developed total purchased privacy maximisation (TPPM) principle for procurement.
Proposed techniques: attribute fusion and aggregation conditioning for querying.
Validated three query mechanisms on real-world datasets.
Achieved m-PLDP across diverse scenarios.
Demonstrated significant linkage between query accuracy and m-PLDP.
Validated the effectiveness of the proposed techniques through empirical research.

Zhang et al. (2026) studied this question.

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