# Age-Dependent Differential Privacy

arXiv: https://arxiv.org/abs/2209.01466 · HTML: https://mlsanity.com/paper/2209.01466/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [Age Aware Scheduling for Differentially-Private Federated Learning](https://mlsanity.com/paper/2405.05962/) (2024)
- [Learning from Label Proportions and Covariate-shifted Instances](https://mlsanity.com/paper/2411.12334/) (2024)
- [Correlated-Sequence Differential Privacy](https://mlsanity.com/paper/2511.18025/) (2025)
- [FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning](https://mlsanity.com/paper/2604.10499/) (2026)

## Abstract (arXiv)

The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of textitage of information. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, which may lead to unnecessary accuracy loss when trying to achieve meaningful privacy guarantees in time-varying databases. In this work, we introduce textitage-dependent DP, taking into account the underlying stochastic nature of a time-varying database. In this new framework, we establish a connection between classical DP and age-dependent DP, based on which we characterize the impact of data staleness and temporal correlation on privacy guarantees. Our characterization demonstrates that textitaging, i.e., using stale data inputs and/or postponing the release of outputs, can be a new strategy to protect data privacy in addition to noise injection in the traditional DP framework. Furthermore, to generalize our results to a multi-query scenario, we present a sequential composition result for age-dependent DP under any publishing and aging policies. We then characterize the optimal tradeoffs between privacy risk and utility and show how this can be achieved. Finally, case studies show that to achieve a target of an arbitrarily small privacy risk in a single-query case, combing aging and noise injection only leads to a bounded accuracy loss, whereas using noise injection only (as in the benchmark case of DP) will lead to an unbounded accuracy loss.