Pith. sign in

REVIEW 10 cited by

Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2309.11765 v2 pith:H5QF55QR submitted 2023-09-21 cs.LG cs.CR

Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation

Xinyu Tang , Richard Shin , Huseyin A. Inan , Andre Manoel , Fatemehsadat Mireshghallah , Zinan Lin , Sivakanth Gopi , Janardhan Kulkarni
show 1 more author
Robert Sim
This is my paper
classification cs.LG cs.CR
keywords privacyprivatealgorithmachievefew-shotin-contextlearningllms
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the private examples demonstrated in the prompt. We propose a novel algorithm that generates synthetic few-shot demonstrations from the private dataset with formal differential privacy (DP) guarantees, and show empirically that it can achieve effective ICL. We conduct extensive experiments on standard benchmarks and compare our algorithm with non-private ICL and zero-shot solutions. Our results demonstrate that our algorithm can achieve competitive performance with strong privacy levels. These results open up new possibilities for ICL with privacy protection for a broad range of applications.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Not All Entities are Created Equal: A Dynamic Anonymization Framework for Privacy-Preserving RAG

    cs.CR 2026-03 unverdicted novelty 7.0

    TRIP-RAG dynamically anonymizes only high-risk entities in RAG knowledge bases via three context-aware metrics, achieving privacy comparable to full anonymization with under 35% recall drop and up to 56% better genera...

  2. SynBench: A Benchmark for Differentially Private Text Generation

    cs.AI 2025-09 conditional novelty 7.0

    SynBench benchmarks DP text generators across nine datasets and uses a new MIA to show that public pre-training on portions of private data overestimates synthetic text quality and breaks DP privacy bounds.

  3. Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models

    cs.CR 2026-05 unverdicted novelty 6.0

    PopQuiz Attack infers LLM training data membership by turning examples into quiz questions and measuring answer accuracy, reaching 0.873 average ROC-AUC across six models and outperforming prior methods by 20.6%.

  4. Not All Entities are Created Equal: A Dynamic Anonymization Framework for Privacy-Preserving RAG

    cs.CR 2026-03 conditional novelty 6.0

    Context-aware selective entity anonymization for RAG matches full-anonymization privacy with <35% Recall@k drop and up to 56% better generation quality than baselines.

  5. SnapAudit: Active Auditing of Differentially Private In-Context Learning via Snapshot-Based Simulation

    cs.CR 2025-11 conditional novelty 6.0

    SnapAudit decomposes DP-ICL into a deterministic snapshot stage and a stochastic noise stage, using bootstrap simulation to achieve 80-200x faster auditing and exposing privacy bound violations in existing Gaussian an...

  6. InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy

    cs.LG 2025-06 unverdicted novelty 6.0

    InvisibleInk achieves high-utility differentially private long-form LLM text generation at 4-8x the cost of non-private generation by isolating and clipping sensitive logits and sampling from a small superset of top-k...

  7. Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents

    cs.CR 2026-06 unverdicted novelty 5.0

    A data-centric survey finds that only information-flow control covers compositional and cross-session leakage in LLM agents and that no single benchmark tests an agent across all its data surfaces under one policy.

  8. Are Large Language Models Suitable for Graph Computation? Progress and Prospects

    cs.CL 2026-06 unverdicted novelty 4.0

    A survey of LLMs for graph computation introduces a role-based taxonomy of executors versus planners and concludes that current models suit simple small-scale tasks but remain unreliable for large-scale exact computation.

  9. Public Data Assisted Differentially Private In-Context Learning

    cs.AI 2025-09 conditional novelty 4.0

    A private ICL algorithm that aggregates LLM responses with DPM clustering and uses public data representatives achieves near-non-private utility at epsilon=1.

  10. Diverse And Private Synthetic Datasets Generation for RAG evaluation: A multi-agent framework

    cs.CL 2025-08 conditional novelty 4.0

    A multi-agent LLM framework generates synthetic QA datasets for RAG evaluation by combining clustering-based sampling, PII pseudonymization, and QA curation, with reported diversity gains and 0.75-0.90 masking accuracy.