Open Problems

Differential Privacy

Robustness and Sensitivity of Public-Data-Assisted Differential Privacy Under Distribution Shift and Proxy Misspecification

Effect to explainOpen
Possible candidate · 2/5 runs4 papers report this75% from 2025+

Generated automatically from the limitations stated in 4 papers (AISTATS, ICML, NeurIPS), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Differentially private algorithms increasingly rely on auxiliary public data or pre-set structural parameters—such as covariance proxies, compact feature bounds, and distributional similarity—to mitigate noise. However, in realistic deployments, public datasets rarely match private target distributions exactly, and assuming tight compact bounds or positive-definite covariance proxies can fail. When these auxiliary assumptions degrade, algorithms either suffer severe utility loss or must spend substantial privacy budget to estimate these quantities from private data. Without a systematic evaluation of how public-assisted DP methods perform across varying degrees of proxy error, bound truncation, and distribution shift, practitioners cannot determine when auxiliary public data provides a net benefit over purely private baselines.

Why it matters

Practitioners can evaluate whether a candidate public dataset will improve or degrade private learning utility before committing privacy budget, and algorithm designers gain concrete empirical baselines for DP algorithms robust to auxiliary distribution shifts.

Ways to approach it

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  1. 1

    Benchmark existing public-data-assisted DP algorithms (e.g., private regression with covariance proxies, public-feature learning, and DP fine-tuning) across controlled synthetic and semi-synthetic distribution shifts (covariate shift, concept shift, label shift). Measure downstream task utility and privacy budget overhead across varying levels of similarity ($\gamma$) and shift magnitude.

  2. 2

    Quantify the sensitivity of DP algorithms to covariance proxy misspecification (condition number, rank deficiency) and domain bound truncation (clipping threshold error vs. private bound estimation cost), measuring the exact point where auxiliary data assistance ceases to outperform standard DP-SGD or unassisted private baselines.

  3. 3

    Construct a suite of diagnostic tests that estimate whether an available public auxiliary dataset is sufficiently aligned to improve private utility before allocating privacy budget to joint estimation.

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Why it might fail

If the community shifts entirely away from auxiliary public data assistance toward foundation-model zero-shot private adaptation, or if simple DP pre-processing heuristics (such as adaptive quantile clipping) already neutralize most sensitivity to distribution shift in practice.

Evidence

Each paper's own statement of the limitation, verbatim.

Nearest existing work

Generated automatically, not curated by hand. Automated prior-work checks catch about a third of existing work, so treat this problem as a lead to investigate.