Privacy
Differential privacy, federated learning, and privacy-preserving ML
20 papers indexed
Digital Privacy in IoT: Exploring Challenges, Approaches and Open Issues
This paper analyzes digital privacy risks in IoT ecosystems, proposing a comprehensive framework (AURA-IoT) and taxonomy to mitigate threats using advanced privacy-enhancing technologies.
FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
FedFG introduces a robust federated learning framework using flow-matching generation to simultaneously enhance client privacy and defend against sophisticated poisoning attacks.
DDP-SA: Scalable Privacy-Preserving Federated Learning via Distributed Differential Privacy and Secure Aggregation
DDP-SA is a novel federated learning framework that combines local differential privacy and secure aggregation to achieve robust, scalable, and highly private model training.
Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
The paper proposes PINA, a two-stage differentially private clustered federated learning framework that improves convergence and robustness by using low-rank adaptation and a normality-driven aggregat…
Towards Privacy-Preserving Federated Learning using Hybrid Homomorphic Encryption
Ivan Costa, Pedro Correia, Ivone Amorim, Eva Maia +1 more
This paper enhances Federated Learning privacy by integrating two key protection mechanisms—masking and RSA encapsulation—into Hybrid Homomorphic Encryption (HHE) to secure against malicious clients.
Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation
The paper proposes RPSG, a method that uses private seeds and differential privacy to generate highly realistic and strongly privacy-preserving synthetic data replicas of private text for LLMs.
FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning
FedAttr introduces a novel client-level attribution protocol for Federated Learning (FL) that accurately identifies which clients trained on watermarked data while maintaining strong privacy guarantee…
Improving Parameter-Efficient Federated Learning with Differentially Private Refactorization
The paper proposes FedPower, a novel differentially private cross-silo Federated Learning framework that uses PowerDP to reconstruct and project client updates into a secure low-rank space, effectivel…
Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
The paper proposes a proactive client selection framework that optimizes the selection of client subsets to ensure high data utility and fairness before federated learning begins, leading to faster an…
Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks
Yanming Mu, Hao Hu, Feiyang Li, Qiao Yuan +6 more
This paper provides the first comprehensive, end-to-end survey dedicated to the security of Retrieval-Augmented Generation (RAG) systems, systematically mapping threats, defenses, and benchmarks acros…
PAC-DP: Personalized Adaptive Clipping for Differentially Private Federated Learning
The paper proposes PAC-DP, a personalized adaptive clipping framework that dynamically adjusts gradient clipping thresholds based on the desired privacy budget, significantly improving the privacy-uti…
PrivFedTalk: Privacy-Aware Federated Diffusion with Identity-Stable Adapters for Personalized Talking-Head Generation
PrivFedTalk introduces a privacy-aware federated framework for personalized talking-head generation by combining a shared diffusion backbone with local LoRA identity adapters and robust aggregation te…
Multi-Objective Submodular Maximization with Differential Privacy
Ting Hou, Yanhao Wang, Yiping Wang, Cen Chen +2 more
This paper addresses the challenging problem of multi-objective submodular maximization under a cardinality constraint while ensuring differential privacy, proposing novel algorithms with approximatio…
DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs
DP-FLogTinyLLM proposes a privacy-preserving federated framework for log anomaly detection that uses efficient Tiny LLMs, achieving high performance comparable to centralized methods while maintaining…
Differentially Private Datastore Generation for Retrieval-Augmented Inference
The paper proposes a hashing-based framework using Differential Privacy to generate and release private datastores for retrieval-augmented AI systems, achieving strong privacy with minimal accuracy lo…
SoK: Practical Aspects of Releasing Differentially Private Graphs
This paper provides a comprehensive, practitioner-oriented framework and survey to guide the selection and evaluation of differentially private methods for releasing sensitive graph data.
DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models
Haichao Sha, Zihao Wang, Yuncheng Wu, Hong Chen +1 more
The paper proposes DP-SelFT, a novel framework for differentially private selective fine-tuning that significantly improves the privacy-utility trade-off for LLMs by intelligently selecting robust par…
Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions
The paper proposes Byz-Clip21-SGD2M, a novel algorithm that achieves high-probability convergence guarantees for Federated Learning by integrating robust aggregation, double momentum, and clipping, re…
Protecting K-Nearest Neighbor Queries from Location Inference Attacks
Zhiyu Sun, Jie Fu, Xinpeng Ling, Huifa Li +1 more
This paper identifies two novel location inference attacks against k-nearest neighbor queries (kNNQ) and proposes DPRS, a differential privacy framework that effectively protects location privacy whil…
Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
This paper proposes a comprehensive federated learning workflow that enhances privacy and robustness by integrating personalized differential privacy budgets and client drift detection, achieving bett…