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PIAFGNN:Property Inference Attacks against Federated Graph Neural Networks

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Computers, Materials & Continua 2025年第2期82卷 1857-1877页

作者： Jiewen Liu Bing Chen Baolu Xue Mengya Guo Yuntao Xu College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing321002China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023China

Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack (PIA) against FedGNNs. Compared with prior works on centralized GNNs, in PIAFGNN, the attacker can only obtain the global embedding gradient distributed by the central server. The attacker converts the task of stealing the target user’s local embeddings into a regression problem, using a regression model to generate the target graph node embeddings. By training shadow models and property classifiers, the attacker can infer the basic property information within the target graph that is of interest. Experiments on three benchmark graph datasets demonstrate that PIAFGNN achieves attack accuracy of over 70% in most cases, even approaching the attack accuracy of inference attacks against centralized GNNs in some instances, which is much higher than the attack accuracy of the random guessing method. Furthermore, we observe that common defense mechanisms cannot mitigate our attack without affecting the model’s performance on mainly classification tasks.

关键词： Federated graph neural networks GNNs privacy leakage regression model property inference attacks embeddings

TPII:tracking personally identifiable information via user behaviors in HTTP traffic

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Frontiers of Computer Science 2020年第3期14卷 175-187页

作者： Yi LIU Tian SONG Lejian LIAO School of Computer Science Beijing Institute of TechnologyBeijing 100081China Network Information Center Yan'an UniversityYan'an 716000China School of Computer ScienceBeijing Institute of TechnologyBeijing 100081China

It is widely common that mobile applications collect non-critical personally identifiable information(PII)from users'devices to the cloud by application service providers(ASPs)in a positive manner to provide precise and recommending ***,Internet service providers(ISPs)or local network providers also have strong requirements to collect PIIs for finer-grained traffic control and security ***,it is a challenge to locate PIIs accurately in the massive data of network traffic just like looking a needle in a *** this paper,we address this challenge by presenting an efficient and light-weight approach,namely TPII,which can locate and track PIIs from the HTTP layer rebuilt from raw network *** approach only collects three features from HTTP fields as users'behaviors and then establishes a tree-based decision model to dig PIIs efficiently and *** any priori knowledge,TPII can identify any types of PIIs from any mobile applications,which has a broad vision of *** evaluate the proposed approach on a real dataset collected from a campus network with more than 13k *** experimental results show that the precision and recall of TPII are 91.72%and 94.51%respectively and a parallel implementation of TPII can achieve 213 million records digging and labelling within one hour,reaching near to support 1Gbps wirespeed inspection in *** approach provides network service providers a practical way to collect PIIs for better services.

关键词： network traffic analysis personally identifiable information privacy leakage mobile applications HTTP

DroidEcho:an in-depth dissection of malicious behaviors in Android applications

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Cybersecurity 2018年第1期1卷 126-142页

作者： Guozhu Meng Ruitao Feng Guangdong Bai Kai Chen Yang Liu SKLOIS Institute of Information EngineeringChinese Academy of SciencesBeijingChina Nanyang Technological University SingaporeSingapore Singapore Institute of Technology SingaporeSingapore School of Cyber Security University of Chinese Academy of SciencesBeijingChina

A precise representation for attacks can benefit the detection of malware in both accuracy and ***,it is still far from expectation to describe attacks precisely on the Android *** addition,new features on Android,such as communication mechanisms,introduce new challenges and difficulties for attack *** this paper,we propose abstract attack models to precisely capture the semantics of various Android attacks,which include the corresponding targets,involved behaviors as well as their execution ***,we construct a novel graph-based model called the inter-component communication graph(ICCG)to describe the internal control flows and inter-component communications of *** models take into account more communication channel with a maximized preservation of their program *** the guidance of the attack models,we propose a static searching approach to detect attacks hidden in *** reduce false positive rate,we introduce an additional dynamic confirmation step to check whether the detected attacks are false *** show that DROIDECHO can detect attacks in both benchmark and real-world applications effectively and efficiently with a precision of 89.5%.

关键词： Semantic attack model Android malware detection Inter-component communication graph privacy leakage

A survey of privacy protection techniques for mobile devices

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Journal of Communications and Information Networks 2016年第4期1卷 86-92页

作者： ZHANG Lei ZHU Donglai YANG Zhemin SUN Limin YANG Min Fudan University ShangHai 200433China Institute of Information Engineering China Academy of ScienceBeijing 100093China

Modern mobile devices provide a wide variety of *** are able to access these services for many sensitive tasks relating to their everyday lives(e.g.,finance,home,or contacts).However,these services also provide new attack surfaces to *** efforts have been devoted to protecting mobile users from privacy *** this work,we study state-of-the-art techniques for the detection and protection of privacy leakage and discuss the evolving trends of privacy research.

关键词： mobile security privacy leakage privacy protection static analysis dynamic analysis