FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation

Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary features from the passive parties to improve model performance. Concerns about the private feature and label leakage in both the training and inference phases of VFL have drawn wide research attention. In this paper, we propose a general privacy-preserving vertical federated deep learning framework called FedPass, which leverages adaptive obfuscation to protect the feature and label simultaneously. Strong privacy-preserving capabilities about private features and labels are theoretically proved (in Theorems 1 and 2). Extensive experimental result s with different datasets and network architectures also justify the superiority of FedPass against existing methods in light of its near-optimal trade-off between privacy and model performance.

FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders

This paper proposes a general spectral analysis framework that thwarts a security risk in federated Learning caused by groups of malicious Byzantine attackers or colluders, who conspire to upload vicious model updates to severely debase global model performances. The proposed framework delineates the strong consistency and temporal coherence between Byzantine colluders' model updates from a spectral analysis lens, and, formulates the detection of Byzantine misbehaviours as a community detection problem in weighted graphs. The modified normalized graph cut is then utilized to discern attackers from benign participants. Moreover, the Spectral heuristics is adopted to make the detection robust against various attacks. The proposed Byzantine colluder resilient method, i.e., FedCut, is guaranteed to converge with bounded errors. Extensive experimental results under a variety of settings justify the superiority of FedCut, which demonstrates extremely robust model performance (MP) under various attacks. It was shown that FedCut's averaged MP is 2.1% to 16.5% better than that of the state of the art Byzantine-resilient methods. In terms of the worst-case model performance (MP), FedCut is 17.6% to 69.5% better than these methods.

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model

Copyright protection of the Federated Learning (FL) model has become a major concern since malicious clients in FL can stealthily distribute or sell the FL model to other parties. In order to prevent such misbehavior, one must be able to catch the culprit by investigating trace evidence from the model in question. In this paper, we propose FedTracker, the first FL model protection framework that, on one hand, employs global watermarks to verify ownerships of the global model; and on the other hand, embed unique local fingerprints into respective local models to facilitate tracing the model back to the culprit. Furthermore, FedTracker introduces the intuition of Continual Learning (CL) into watermark embedding, and proposes a CL-based watermark mechanism to improve fidelity. Experimental results show that the proposed FedTracker is effective in ownership verification, traceability, fidelity, and robustness.

A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning

Federated learning (FL) has emerged as a practical solution to tackle data silo issues without compromising user privacy. One of its variants, vertical federated learning (VFL), has recently gained increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to build better machine learning models while preserving user privacy. Current works in VFL concentrate on developing a specific protection or attack mechanism for a particular VFL algorithm. In this work, we propose an evaluation framework that formulates the privacy-utility evaluation problem. We then use this framework as a guide to comprehensively evaluate a broad range of protection mechanisms against most of the state-of-the-art privacy attacks for three widely-deployed VFL algorithms. These evaluations may help FL practitioners select appropriate protection mechanisms given specific requirements. Our evaluation results demonstrate that: the model inversion and most of the label inference attacks can be thwarted by existing protection mechanisms; the model completion (MC) attack is difficult to be prevented, which calls for more advanced MC-targeted protection mechanisms. Based on our evaluation results, we offer concrete advice on improving the privacy-preserving capability of VFL systems.

A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning

Federated learning (FL) enables independent parties to collaboratively build machine learning (ML) models while protecting data privacy. Vertical federated learning (VFL), a variant of FL, has recently drawn increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to achieve better model performance without jeopardizing data privacy. However, conventional VFL may run into data deficiency as it is only able to exploit aligned samples (belonging to different parties) with labels, leaving often the majority of unaligned and unlabeled samples unused. The data deficiency hampers the effort of the federation. In this work, we propose a Federated Hybrid Self-Supervised Learning framework, coined FedHSSL, to utilize all available data (including unaligned and unlabeled samples) of participants to train the joint VFL model. The core idea of FedHSSL is to utilize cross-party views (i.e., dispersed features) of samples aligned among parties and local views (i.e., augmentations) of samples within each party to improve the representation learning capability of the joint VFL model through SSL (e.g., SimSiam). FedHSSL further exploits generic features shared among parties to boost the performance of the joint model through partial model aggregation. We empirically demonstrate that our FedHSSL achieves significant performance gains compared with baseline methods, especially when the number of labeled samples is small. We provide an in-depth analysis of FedHSSL regarding privacy leakage, which is rarely discussed in existing self-supervised VFL works. We investigate the protection mechanism for FedHSSL. The results show our protection can thwart the state-of-the-art label inference attack.

FadMan: Federated Anomaly Detection across Multiple Attributed Networks

Anomaly subgraph detection has been widely used in various applications, ranging from cyber attack in computer networks to malicious activities in social networks. Despite an increasing need for federated anomaly detection across multiple attributed networks, only a limited number of approaches are available for this problem. Federated anomaly detection faces two major challenges. One is that isolated data in most industries are restricted share with others for data privacy and security. The other is most of the centralized approaches training based on data integration. The main idea of federated anomaly detection is aligning private anomalies from local data owners on the public anomalies from the attributed network in the server through public anomalies to federate local anomalies. In each private attributed network, the detected anomaly subgraph is aligned with an anomaly subgraph in the public attributed network. The significant public anomaly subgraphs are selected for federated private anomalies while preventing local private data leakage. The proposed algorithm FadMan is a vertical federated learning framework for public node aligned with many private nodes of different features, and is validated on two tasks correlated anomaly detection on multiple attributed networks and anomaly detection on an attributeless network using five real-world datasets. In the first scenario, FadMan outperforms competitive methods by at least 12% accuracy at 10% noise level. In the second scenario, by analyzing the distribution of abnormal nodes, we find that the nodes of traffic anomalies are associated with the event of postgraduate entrance examination on the same day.

No Free Lunch Theorem for Security and Utility in Federated Learning

In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data must be kept secure as much as possible in the presence of \textit{semi-honest} partners, while on the other hand, a certain amount of information has to be exchanged among different parties for the sake of learning utility. Such a challenge calls for the privacy-preserving federated learning solution, which maximizes the utility of the learned model and maintains a provable privacy guarantee of participating parties' private data. This article illustrates a general framework that a) formulates the trade-off between privacy loss and utility loss from a unified information-theoretic point of view, and b) delineates quantitative bounds of privacy-utility trade-off when different protection mechanisms including Randomization, Sparsity, and Homomorphic Encryption are used. It was shown that in general \textit{there is no free lunch for the privacy-utility trade-off} and one had to trade the protection of privacy with a certain degree of degraded utility. The quantitative analysis illustrated in this article may serve as the guidance for the design of practical federated learning algorithms.

No free lunch theorem for security and utility in federated learning

In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data must be kept secure as much as possible in the presence of \textit{semi-honest} partners, while on the other hand, a certain amount of information has to be exchanged among different parties for the sake of learning utility. Such a challenge calls for the privacy-preserving federated learning solution, which maximizes the utility of the learned model and maintains a provable privacy guarantee of participating parties' private data. This article illustrates a general framework that a) formulates the trade-off between privacy loss and utility loss from a unified information-theoretic point of view, and b) delineates quantitative bounds of privacy-utility trade-off when different protection mechanisms including Randomization, Sparsity, and Homomorphic Encryption are used. It was shown that in general \textit{there is no free lunch for the privacy-utility trade-off} and one has to trade the preserving of privacy with a certain degree of degraded utility. The quantitative analysis illustrated in this article may serve as the guidance for the design of practical federated learning algorithms.

FedIPR: Ownership Verification for Federated Deep Neural Network Models

Federated learning models must be protected against plagiarism since these models are built upon valuable training data owned by multiple institutions or people.This paper illustrates a novel federated deep neural network (FedDNN) ownership verification scheme that allows ownership signatures to be embedded and verified to claim legitimate intellectual property rights (IPR) of FedDNN models, in case that models are illegally copied, re-distributed or misused. The effectiveness of embedded ownership signatures is theoretically justified by proved condition sunder which signatures can be embedded and detected by multiple clients with-out disclosing private signatures. Extensive experimental results on CIFAR10,CIFAR100 image datasets demonstrate that varying bit-lengths signatures can be embedded and reliably detected without affecting models classification performances. Signatures are also robust against removal attacks including fine-tuning and pruning.

Federated Deep Learning with Bayesian Privacy

Federated learning (FL) aims to protect data privacy by cooperatively learning a model without sharing private data among users. For Federated Learning of Deep Neural Network with billions of model parameters, existing privacy-preserving solutions are unsatisfactory. Homomorphic encryption (HE) based methods provide secure privacy protections but suffer from extremely high computational and communication overheads rendering it almost useless in practice . Deep learning with Differential Privacy (DP) was implemented as a practical learning algorithm at a manageable cost in complexity. However, DP is vulnerable to aggressive Bayesian restoration attacks as disclosed in the literature and demonstrated in experimental results of this work. To address the aforementioned perplexity, we propose a novel Bayesian Privacy (BP) framework which enables Bayesian restoration attacks to be formulated as the probability of reconstructing private data from observed public information. Specifically, the proposed BP framework accurately quantifies privacy loss by Kullback-Leibler (KL) Divergence between the prior distribution about the privacy data and the posterior distribution of restoration private data conditioning on exposed information}. To our best knowledge, this Bayesian Privacy analysis is the first to provides theoretical justification of secure privacy-preserving capabilities against Bayesian restoration attacks. As a concrete use case, we demonstrate that a novel federated deep learning method using private passport layers is able to simultaneously achieve high model performance, privacy-preserving capability and low computational complexity. Theoretical analysis is in accordance with empirical measurements of information leakage extensively experimented with a variety of DNN networks on image classification MNIST, CIFAR10, and CIFAR100 datasets.