Privacy Framework

Privacy frameworks aim to protect sensitive data used in machine learning, balancing data utility with privacy preservation. Current research focuses on developing and evaluating techniques like differential privacy, homomorphic encryption, and federated learning, often incorporating model architectures such as convolutional neural networks and graph neural networks to achieve this balance. These efforts are crucial for addressing growing concerns about data breaches and misuse in various applications, from healthcare and finance to speech processing and recommendation systems, impacting both the ethical development of AI and the security of sensitive information.

Papers

September 30, 2024

Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
Thomas Schneider, Ajith Suresh, Hossein Yalame
Federated Learning Poisoning Attack Homomorphic Encryption Private Federated Learning Online Comment Privacy Threat Privacy Framework

August 7, 2024

Speech privacy-preserving methods using secret key for convolutional neural network models and their robustness evaluation
Shoko Niwa, Sayaka Shiota, Hitoshi Kiya
Speech Analysis Privacy Preserving Convolutional Neural Network Model Robustness Evaluation Encryption Method Secret Key Privacy Framework

May 31, 2024

A Novel Review of Stability Techniques for Improved Privacy-Preserving Machine Learning
Coleman DuPlessie, Aidan Gao
Machine Learning Machine Learning Model Data Leakage Stability Analysis Book Review Privacy Framework Dataset Level Privacy

May 29, 2024

The Data Minimization Principle in Machine Learning
Prakhar Ganesh, Cuong Tran, Reza Shokri, Ferdinando Fioretto
Machine Learning Minimization Algorithm Privacy Framework Higher Privacy Level Data Minimization

April 4, 2024

Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Data
Okko Makkonen, Sampo Niemelä, Camilla Hollanti, Serge Kas Hanna
Federated Learning Raw Data Gradient Coding Privacy Framework Heterogeneous Label Anonymised Data

March 14, 2024

Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems
Ayoub Si-ahmed, Mohammed Ali Al-Garadi, Narhimene Boustia
Anomaly Detection Intrusion Detection Security Related Intrusion Detection System Interpretable Machine Internet of Medical Thing Privacy Framework

October 27, 2023

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou, Yulia Tsvetkov, Maarten Sap, Reza Shokri, Yejin Choi
Large Language Model Language Model Chain of Thought Privacy Framework Contextual Integrity Secret Source Inference Privacy

August 15, 2023

Decentralized Graph Neural Network for Privacy-Preserving Recommendation
Xiaolin Zheng, Zhongyu Wang, Chaochao Chen, Jiashu Qian, Yao Yang
Privacy Preserving Effective Recommendation Local Differential Privacy Gradient Sharing Privacy Framework Decentralized Graph Neural Network

June 22, 2023

Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems
Theshani Nuradha, Ziv Goldfeld, Mark M. Wilde
Differential Privacy Quantum Algorithm Quantum System Non Markovian Reward Privacy Framework Pufferfish Privacy

March 14, 2022

Reasoning over Public and Private Data in Retrieval-Based Systems
Simran Arora, Patrick Lewis, Angela Fan, Jacob Kahn, Christopher Ré
Complex Reasoning Private Data Retrieval Task Text Benchmark Privacy Level Privacy Framework Private INFORMATION RETRIEVAL

February 14, 2022

Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandal
Felipe González-Pizarro, Andrea Figueroa, Claudia López, Cecilia Aragon
Data Privacy Multiple Source Privacy Framework Privacy Concern

December 28, 2021

Financial Vision Based Differential Privacy Applications
Jun-Hao Chen, Yi-Jen Wang, Yun-Cheng Tsai, Samuel Yen-Chi Chen
Differential Privacy Privacy Guarantee Privacy Framework Trading Data Learning Privacy

Privacy Framework

Papers

Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"

Speech privacy-preserving methods using secret key for convolutional neural network models and their robustness evaluation

A Novel Review of Stability Techniques for Improved Privacy-Preserving Machine Learning

The Data Minimization Principle in Machine Learning

Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Data

Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory

Decentralized Graph Neural Network for Privacy-Preserving Recommendation

Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems

Reasoning over Public and Private Data in Retrieval-Based Systems

Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandal

Financial Vision Based Differential Privacy Applications