利用物理信息机器学习估计不规则用水需求以辅助泄漏检测

Modelling and Quantifying Membership Information Leakage in Machine Learning

Machine learning models have been shown to be vulnerable to membership inference attacks, i.e., inferring whether individuals' data have been used for training models. The lack of understanding about factors contributing success of these attacks motivates the need for modelling membership information leakage using information theory and for investigating properties of machine learning models and training algorithms that can reduce membership information leakage. We use conditional mutual information leakage to measure the amount of information leakage from the trained machine learning model ab…

A Quantitative Metric for Privacy Leakage in Federated Learning

In the federated learning system, parameter gradients are shared among participants and the central modulator, while the original data never leave their protected source domain. However, the gradient itself might carry enough information for precise inference of the original data. By reporting their parameter gradients to the central server, client datasets are exposed to inference attacks from adversaries. In this paper, we propose a quantitative metric based on mutual information for clients to evaluate the potential risk of information leakage in their gradients. Mutual information has rece…

NoPeek: Information leakage reduction to share activations in distributed deep learning

For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining model accuracy. Leakage (measured using distance correlation between input and intermediate representations) is the risk associated with the invertibility of raw data from intermediary representations. This can prevent client entities that hold sensitive data from using distributed deep learning services. We demonstrate that our method is resilient …

Analyzing Information Leakage of Updates to Natural Language Models

To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models. We show that a differential analysis of language model snapshots before and after an update can reveal a surprising amount of detailed information about changes in the training data. We propose two new metrics---\emph{differential score} and \emph{differential rank}---for analyzing the leakage due to updates of natural language models. We perform leakage analysis using these metrics across models trained on several different datasets using different…

Channel Leakage, Information-Theoretic Limitations of Obfuscation, and Optimal Privacy Mask Design for Streaming Data

In this paper, we first introduce the notion of channel leakage as the minimum mutual information between the channel input and channel output. As its name indicates, channel leakage quantifies the minimum information leakage to the malicious receiver. In a broad sense, it can be viewed as a dual concept of channel capacity, which characterizes the maximum information transmission to the targeted receiver. We obtain explicit formulas of channel leakage for the white Gaussian case, the colored Gaussian case, and the fading case. We then utilize this notion to investigate the fundamental limitat…

面向医疗数据民主化与信息泄露防护的数据编码方法

Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention

针对医疗领域数据民主化不足与模型信息泄露问题，提出基于随机投影和随机量子编码的不可逆数据编码方案，在保留数据语义的同时降低模型信息泄露风险。

减少信息泄露与计算量的联邦学习

Federated Learning with Reduced Information Leakage and Computation

本文提出 Upcycled-FL 策略，通过在偶数轮更新中应用一阶近似减少一半联邦学习更新的信息泄露与计算传输开销，并结合扰动机制在多种框架中改善隐私与准确率权衡。

用于配水管网泄漏检测与定位的算法启发图神经网络

Algorithm-Informed Graph Neural Networks for Leakage Detection and Localization in Water Distribution Networks

针对配水管网泄漏检测中GNN分布外泛化不足的问题，本文提出AIGNN，通过预训练模拟Ford-Fulkerson最大流算法并迁移知识，结合压力重建与预测实现更优的泄漏检测定位。

基于模型预测的信息泄露的无先验盲检测

A prior-free blind detection of information leakage from model predictions

针对机器学习模型输出中信息泄露的检测问题，提出决策理论框架，证明校准泄露与诚实模型不可区分，但近确定性子组可被无先验检测，并在UK Biobank上验证。

Distribution inference risks: Identifying and mitigating sources of leakage

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the goal of an adversary is to infer distributional information about the training data. So far, research on distribution inference has focused on demonstrating successful attacks, with little attention given to identifying the potential causes of the leakage and to proposing mitigations. To bridge this gap, as our main contribution, we theoreti…

通过近似贝叶斯最优预测的信息泄露检测

Information Leakage Detection through Approximate Bayes-optimal Prediction

提出基于统计学习与信息论的理论框架，通过自动机器学习近似贝叶斯预测器的对数损失和准确率来估计互信息，从而检测信息泄露，在合成和真实OpenSSL TLS服务器数据集上优于现有方法。

Bounding Information Leakage in Machine Learning

Recently, it has been shown that Machine Learning models can leak sensitive information about their training data. This information leakage is exposed through membership and attribute inference attacks. Although many attack strategies have been proposed, little effort has been made to formalize these problems. We present a novel formalism, generalizing membership and attribute inference attack setups previously studied in the literature and connecting them to memorization and generalization. First, we derive a universal bound on the success rate of inference attacks and connect it to the gener…