# Context-aware Data Aggregation with Localized Information Privacy

arXiv: https://arxiv.org/abs/1804.02149 · HTML: https://mlsanity.com/paper/1804.02149/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2018.

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

- [Bridging Privacy and Robustness for Trustworthy Machine Learning](https://mlsanity.com/paper/2403.16591/) (2024)
- [Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2508.11907/) (2025)

## Abstract (arXiv)

In this paper, localized information privacy (LIP) is proposed, as a new privacy definition, which allows statistical aggregation while protecting users' privacy without relying on a trusted third party. The notion of context-awareness is incorporated in LIP by the introduction of priors, which enables the design of privacy-preserving data aggregation with knowledge of priors. We show that LIP relaxes the Localized Differential Privacy (LDP) notion by explicitly modeling the adversary's knowledge. However, it is stricter than 2ε-LDP and ε-mutual information privacy. The incorporation of local priors allows LIP to achieve higher utility compared to other approaches. We then present an optimization framework for privacy-preserving data aggregation, with the goal of minimizing the expected squared error while satisfying the LIP privacy constraints. Utility-privacy tradeoffs are obtained under several models in closed-form. We then validate our analysis by numerical analysis using both synthetic and real-world data. Results show that our LIP mechanism provides better utility-privacy tradeoffs than LDP and when the prior is not uniformly distributed, the advantage of LIP is even more significant.