arXiv:2605.30476v1 Announce Type: cross Abstract: We study privately estimating the sum of $n$ user-held values in the presence of an honest-but-curious server. This motivates requiring privacy not only at data release but also throughout server-side computation. We therefore adopt the local (pure) differential privacy model, in which each user transmits a noise-perturbed value. It is well known that independent local noise typically incurs a substantial utility loss compared to the centralized model, where noise is added only after aggregation. We show that this gap is not fundamental. By car
The increasing focus on privacy in AI and data processing, particularly within distributed systems, necessitates innovations in privacy-preserving techniques like differential privacy.
This research addresses a critical limitation of local differential privacy — the utility loss compared to centralized models — an essential step for broader adoption of privacy-preserving AI.
The demonstrated ability to achieve optimal cost in local differential privacy via correlated noise could make privacy-preserving data aggregation more practical and efficient, reducing the trade-off between privacy and utility.
- · AI researchers
- · Privacy-focused technology companies
- · Users of privacy-preserving distributed systems
Improved utility for local differential privacy implementations in various AI applications.
Increased adoption of distributed privacy-preserving machine learning due to better performance.
New regulatory frameworks or industry standards may emerge that leverage these more efficient privacy techniques.
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Read at arXiv cs.LG