PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization
Fuente:
arXiv
Saved in:
| Main Authors: | Ertan, Murat Bilgehan, Zhu, Xiaochen, Nguyen, Phuong Ha, van Dijk, Marten, Devadas, Srinivas |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds
by: van Dijk, Marten, et al.
Published: (2026)
by: van Dijk, Marten, et al.
Published: (2026)
Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
by: Ertan, Murat Bilgehan, et al.
Published: (2026)
by: Ertan, Murat Bilgehan, et al.
Published: (2026)
PAC-Private Responses with Adversarial Composition
by: Zhu, Xiaochen, et al.
Published: (2026)
by: Zhu, Xiaochen, et al.
Published: (2026)
On the Evidentiary Limits of Membership Inference for Copyright Auditing
by: Ertan, Murat Bilgehan, et al.
Published: (2026)
by: Ertan, Murat Bilgehan, et al.
Published: (2026)
Breaking XOR Arbiter PUFs with Chosen Challenge Attack
by: Sayadi, Niloufar, et al.
Published: (2023)
by: Sayadi, Niloufar, et al.
Published: (2023)
Making Sense of Private Advertising: A Principled Approach to a Complex Ecosystem
by: Hogan, Kyle, et al.
Published: (2025)
by: Hogan, Kyle, et al.
Published: (2025)
DP-TLDM: Differentially Private Tabular Latent Diffusion Model
by: Zhu, Chaoyi, et al.
Published: (2024)
by: Zhu, Chaoyi, et al.
Published: (2024)
TOSSS: a CVE-based Software Security Benchmark for Large Language Models
by: Damie, Marc, et al.
Published: (2026)
by: Damie, Marc, et al.
Published: (2026)
PAC to the Future: Zero-Knowledge Proofs of PAC Private Systems
by: Repetto, Guilhem, et al.
Published: (2026)
by: Repetto, Guilhem, et al.
Published: (2026)
Differentially Private Subspace Fine-Tuning for Large Language Models
by: Zheng, Lele, et al.
Published: (2026)
by: Zheng, Lele, et al.
Published: (2026)
Efficient Differentially Private Fine-Tuning of Diffusion Models
by: Liu, Jing, et al.
Published: (2024)
by: Liu, Jing, et al.
Published: (2024)
Beyond Anonymization: Object Scrubbing for Privacy-Preserving 2D and 3D Vision Tasks
by: Ertan, Murat Bilgehan, et al.
Published: (2025)
by: Ertan, Murat Bilgehan, et al.
Published: (2025)
ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models
by: Xie, Jin, et al.
Published: (2025)
by: Xie, Jin, et al.
Published: (2025)
When FinTech Meets Privacy: Securing Financial LLMs with Differential Private Fine-Tuning
by: Zhu, Sichen, et al.
Published: (2025)
by: Zhu, Sichen, et al.
Published: (2025)
CryptPEFT: Efficient and Private Neural Network Inference via Parameter-Efficient Fine-Tuning
by: Xia, Saisai, et al.
Published: (2025)
by: Xia, Saisai, et al.
Published: (2025)
DPZero: Private Fine-Tuning of Language Models without Backpropagation
by: Zhang, Liang, et al.
Published: (2023)
by: Zhang, Liang, et al.
Published: (2023)
DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models
by: Sha, Haichao, et al.
Published: (2026)
by: Sha, Haichao, et al.
Published: (2026)
Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models
by: Wei, Qianshan, et al.
Published: (2025)
by: Wei, Qianshan, et al.
Published: (2025)
Citadel: Simple Spectre-Safe Isolation For Real-World Programs That Share Memory
by: Drean, Jules, et al.
Published: (2023)
by: Drean, Jules, et al.
Published: (2023)
Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)
by: Yang, Hillary, et al.
Published: (2024)
Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes
by: Kumar, Divyanshu, et al.
Published: (2024)
by: Kumar, Divyanshu, et al.
Published: (2024)
Benchmarking Differentially Private Tabular Data Synthesis
by: Chen, Kai, et al.
Published: (2025)
by: Chen, Kai, et al.
Published: (2025)
Function Recovery Attacks in Gate-Hiding Garbled Circuits using SAT Solving
by: Yin, Chao, et al.
Published: (2026)
by: Yin, Chao, et al.
Published: (2026)
LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models
by: Yang, Qin, et al.
Published: (2024)
by: Yang, Qin, et al.
Published: (2024)
Differentially Private Fine-Tuning of Diffusion Models
by: Tsai, Yu-Lin, et al.
Published: (2024)
by: Tsai, Yu-Lin, et al.
Published: (2024)
Differentially Private and Communication Efficient Large Language Model Split Inference via Stochastic Quantization and Soft Prompt
by: Gu, Yujie, et al.
Published: (2026)
by: Gu, Yujie, et al.
Published: (2026)
Differentially Private Parameter-Efficient Fine-tuning for Large ASR Models
by: Liu, Hongbin, et al.
Published: (2024)
by: Liu, Hongbin, et al.
Published: (2024)
Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning
by: Chen, Chaoran, et al.
Published: (2026)
by: Chen, Chaoran, et al.
Published: (2026)
Unveiling the Vulnerability of Private Fine-Tuning in Split-Based Frameworks for Large Language Models: A Bidirectionally Enhanced Attack
by: Chen, Guanzhong, et al.
Published: (2024)
by: Chen, Guanzhong, et al.
Published: (2024)
HeteroFedSyn: Differentially Private Tabular Data Synthesis for Heterogeneous Federated Settings
by: Li, Xiaochen, et al.
Published: (2026)
by: Li, Xiaochen, et al.
Published: (2026)
In-Context Probing for Membership Inference in Fine-Tuned Language Models
by: Lu, Zhexi, et al.
Published: (2025)
by: Lu, Zhexi, et al.
Published: (2025)
PRIVMARK: Private Large Language Models Watermarking with MPC
by: Fargues, Thomas, et al.
Published: (2025)
by: Fargues, Thomas, et al.
Published: (2025)
Private Iris Recognition with High-Performance FHE
by: Ha, Jincheol, et al.
Published: (2026)
by: Ha, Jincheol, et al.
Published: (2026)
PrivATE: Differentially Private Average Treatment Effect Estimation for Observational Data
by: Yuan, Quan, et al.
Published: (2025)
by: Yuan, Quan, et al.
Published: (2025)
DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis
by: Gong, Chen, et al.
Published: (2026)
by: Gong, Chen, et al.
Published: (2026)
EQO: Exploring Ultra-Efficient Private Inference with Winograd-Based Protocol and Quantization Co-Optimization
by: Zeng, Wenxuan, et al.
Published: (2024)
by: Zeng, Wenxuan, et al.
Published: (2024)
Private PAC Learning May be Harder than Online Learning
by: Bun, Mark, et al.
Published: (2024)
by: Bun, Mark, et al.
Published: (2024)
RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis
by: Wang, Jianwei, et al.
Published: (2025)
by: Wang, Jianwei, et al.
Published: (2025)
VeriLoRA: Fine-Tuning Large Language Models with Verifiable Security via Zero-Knowledge Proofs
by: Liao, Guofu, et al.
Published: (2025)
by: Liao, Guofu, et al.
Published: (2025)
Cryptanalysis of a Privacy-Preserving Ride-Hailing Service from NSS 2022
by: Vivek, Srinivas
Published: (2025)
by: Vivek, Srinivas
Published: (2025)
Similar Items
-
Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds
by: van Dijk, Marten, et al.
Published: (2026) -
Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
by: Ertan, Murat Bilgehan, et al.
Published: (2026) -
PAC-Private Responses with Adversarial Composition
by: Zhu, Xiaochen, et al.
Published: (2026) -
On the Evidentiary Limits of Membership Inference for Copyright Auditing
by: Ertan, Murat Bilgehan, et al.
Published: (2026) -
Breaking XOR Arbiter PUFs with Chosen Challenge Attack
by: Sayadi, Niloufar, et al.
Published: (2023)