Privacy Without Regret: Differentially Private Inference-Time Alignment
Best-of-N (BoN) sampling, a widely used inference-time alignment strategy, suffers from reward hacking and lacks privacy protection for sensitive human preference data Adding calibrated Gumbel noise to reward scores before selection simultaneously achieves differential privacy and KL-regularized alignment, eliminating the need for separate regularization Private Best-of-N (PrivBoN) proves that when the privacy budget exceeds a critical threshold ε*, the privacy-mandated noise becomes regret-opti
Analysis
TL;DR
- Best-of-N (BoN) sampling, a widely used inference-time alignment strategy, suffers from reward hacking and lacks privacy protection for sensitive human preference data
- Adding calibrated Gumbel noise to reward scores before selection simultaneously achieves differential privacy and KL-regularized alignment, eliminating the need for separate regularization
- Private Best-of-N (PrivBoN) proves that when the privacy budget exceeds a critical threshold ε*, the privacy-mandated noise becomes regret-optimal regularization with zero additional alignment cost
- Private Inference-Time Pessimism (PrivITP) combines χ²-regularized rejection sampling with a two-phase Gaussian mechanism, achieving ex-post (ε,δ)-DP with privacy cost independent of response count n
- Experiments show both PrivBoN and PrivITP are scaling-monotonic (unlike BoN which degrades past a critical n), with PrivITP matching or outperforming PrivBoN, especially in strong-privacy regimes
Why It Matters
This work bridges two critical concerns in production LLM deployment—alignment quality and data privacy—by showing they can be addressed simultaneously through a single mechanism rather than requiring separate, potentially conflicting solutions. For AI practitioners deploying reward-model-based alignment at inference time, this provides a theoretically grounded, privacy-preserving alternative that avoids the well-known reward hacking problem while protecting the sensitive human preference data underlying reward model training.
Technical Details
- PrivBoN: Adds Gumbel noise at a calibrated scale to reward scores before Best-of-N selection. This noise simultaneously provides ε-differential privacy and implements KL-regularized alignment, with the privacy-mandated noise becoming regret-optimal when ε > ε*
- PrivITP: A more robust method combining χ²-regularized rejection sampling with a two-phase Gaussian mechanism, achieving ex-post (ε,δ)-differential privacy. It cleanly decouples the regularization parameter from the privacy parameter
- Critical threshold ε*: Depends on an unknown coverage coefficient, motivating the introduction of PrivITP as a practical alternative that does not require this knowledge
- Scaling behavior: Unlike standard BoN which degrades beyond a critical number of sampled responses n, both PrivBoN and PrivITP are scaling-monotonic, maintaining or improving performance as n increases
- Experimental validation: Tested across multiple language models, datasets, and reward models, confirming PrivITP matches or exceeds PrivBoN performance at equivalent privacy levels, with largest gains in strong-privacy regimes
Industry Insight
- The theoretical equivalence between differential privacy and KL-regularization suggests that privacy-preserving inference-time alignment may become a standard requirement as regulatory frameworks tighten around training data provenance, making these techniques increasingly relevant for production deployments
- The scaling-monotonic property of PrivBoN and PrivITP addresses a practical pain point: practitioners can safely increase N for better alignment without the degradation risk inherent in standard BoN, simplifying hyperparameter tuning
- The decoupling of privacy and regularization parameters in PrivITP offers operational flexibility, allowing teams to independently tune privacy guarantees and alignment quality—a significant advantage for organizations navigating evolving compliance requirements
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