# From Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy

arXiv: https://arxiv.org/abs/2504.17523 · HTML: https://mlsanity.com/paper/2504.17523/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response](https://mlsanity.com/paper/2509.13987/) (2025)
- [Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap](https://mlsanity.com/paper/2603.13570/) (2026)

## Abstract (arXiv)

Local Differential Privacy (LDP) is the predominant privacy model for safeguarding individual data privacy. Existing perturbation mechanisms typically require perturbing the original values to ensure acceptable privacy, which inevitably results in value distortion and utility deterioration. In this work, we propose an alternative approach -- instead of perturbing values, we apply randomization to indexes of values while ensuring rigorous LDP guarantees. Inspired by the deniability of randomized indexes, we present CRIAD for answering subset counting queries on set-value data. By integrating a multi-dummy, multi-sample, and multi-group strategy, CRIAD serves as a fully scalable solution that offers flexibility across various privacy requirements and domain sizes, and achieves more accurate query results than any existing methods. Through comprehensive theoretical analysis and extensive experimental evaluations, we validate the effectiveness of CRIAD and demonstrate its superiority over traditional value-perturbation mechanisms.