Privacy Leakage Issues Research Articles

Analysis of Personal Privacy Leakage under the Background of Digital Twin

With the development and application of digital twin technology, the problem of privacy leakage is becoming increasingly prominent. Privacy leakage issues caused by data abuse and weak awareness have attracted high attention. In this regard, this paper analyzes the causes of privacy leakage in the digital twin, analyzes the difficulties of privacy protection under the background of the digital twin, and puts forward the “legal co-governance model.” Through multi-party cooperation, improving the system, and improving literacy, people can effectively protect private information, ensure the security application of technology, law, and ethics, and provide a guarantee for the sustainable development and social application of digital twin technology.

Smart industrial information integration: a lightweight privacy protection model in an intelligent manufacturing architecture

In the industrial ecosystem, both the manufacturing and edge clouds collaborate via virtualisation technology, playing a crucial role in the efficient aggregation and processing of vast data from endpoints, offering computational and analytical support for enhanced decision-making in all industrial operations. Consequently, ensuring the reliability of virtualised manufacturing environments is paramount. Remote attestation offers a viable solution, however, current schemes are plagued by inefficiencies and privacy leakage issues. To address these concerns, we propose an efficient remote attestation approach. The approach actively conceals sensitive industrial information in measurement logs by using a hash algorithm, thereby preventing privacy breaches. Furthermore, it streamlines log entries with a log compression algorithm, enhancing remote verification efficiency in the industrial information architecture. Extensive experiments were conducted in an authentic cloud environment by applying this approach across both mesh and hierarchical remote verification structures, reducing time overhead by approximately 81.32%. Additionally, it marginally impacts server performance with only a 1.49% reduction. The results affirm the proposed approach's exceptional efficacy in decreasing verification durations and mitigating remote verification attacks, demonstrating its promising utility and feasibility in the intelligent industrial platform.

DP-FedEwc: Differentially private federated elastic weight consolidation for model personalization

Federated learning (FL) has become a prevalent paradigm for training a model collaboratively on multiple clients with the coordination of a central server. As traditional FL suffers from client-drift due to data heterogeneity across clients, many personalized FL (PFL) techniques have been proposed. However, the issue of privacy leakage within PFL remains inadequately addressed. When incorporating differential privacy (DP) directly into PFL to provide rigorous privacy guarantees, it amplifies heterogeneity among clients and introduces high variance in uploaded information, significantly compromising model’s utility. In this paper, we propose a novel privacy-preserving PFL framework called Differentially Private Federated Elastic weight consolidation (DP-FedEwc), to achieve effective model personalization for each client under sample-level DP. We focus on a practical setting where the server is honest-but-curious. We first implement a FedEwc algorithm in a communication-efficient manner, and provide privacy guarantees by perturbing models and their parameter importance (PI). We show that FedEwc is robust to DP-introduced heterogeneity caused by noisy models, especially when the model is a deep neural network. Since excessive noise may render PI invalid, we present an Adaptive Parameter importance Perturbation (APP) method to adaptively add Gaussian noise to different coordinates of PI, thereby alleviating the negative effect of DP noise. Moreover, to accurately calibrate the privacy cost resulting from querying heterogeneous data across various clients when computing PI through APP, we adapt a Bayesian Accountant (BA) method to DP-FedEwc. We conduct experiments on standard benchmark datasets, and the experimental results confirm the superiority of DP-FedEwc over DP-PFL baselines.

A Self-Sovereign Identity Privacy-Preserving Scheme for Logistics Transportation Based on One-Time-Use Tokens

The advancement of the logistics industry has fostered the enhancement of operational efficacy within the socioeconomic domain. However, the current inevitable privacy leaks in the process of logistics transportation have seriously affected the development of the industry, which led to a crisis of consumer trust and even caused economic recession. This paper proposes a self-sovereign identity privacy protection scheme tailored specifically for logistics transportation contexts. First, the scheme entails furnishing users with one-time-use tokens while establishing decentralized identities capable of concealing identity information and ensuring the secure transmission of data. Furthermore, the scheme integrates fuzzy identity-based encryption to encrypt identity information, thereby guaranteeing the confidentiality and integrity of logistics user identities along with their associated data. Compared with other schemes, this scheme exhibits superior security in the realm of logistics transportation. Its targeted encryption technology and self-sovereignty strategies address the critical issue of privacy leakage, thereby safeguarding consumer privacy rights and interests while facilitating the sustainable development of the logistics industry.

Research on privacy leakage of celebrity's ID card number based on real‐name authentication

AbstractThe Internet real‐name system is widely implemented among Chinese Internet users, and many commonly used apps in China exist the functions of real‐name authentication. However, our study found that many apps do not have effective restrictions on user's operations of real‐name authentication, resulting in users being able to frequently perform unsuccessful real‐name authentication attempts. This vulnerability can help an attacker crack celebrity's ID card number by enumeration attacks, and a feasible cracking method was proposed in this paper. First, the information of birth date, birth place, and life experiences of a celebrity is collected from the platforms that display celebrities' personal information (e.g., Wikipedia, Baidu Baike, etc.). In this process, an information extraction method is used to infer permanent residences from life experiences. Then, the possible ID card numbers of a celebrity can be constructed by using the information of birth date, birth place, and permanent residences. Finally, these possible ID card numbers will be verified by sending requests to platforms that have vulnerabilities in the function of user real‐name authentication, until the real ID card number of a celebrity being cracked. This paper conducted cracking experiments on two groups of celebrities. The first group of celebrities is collected from the news events of privacy leakage that were publicly available online, and the second group of celebrities is randomly selected from two encyclopedia platforms. The experimental results showed that the success rate of cracking the ID card numbers of celebrities is 53.9%, which verified the effectiveness of the proposed cracking method. Besides, this paper proposed some security precaution suggestions to solve this security problem, and the implementation, feasibility, potential impact, expected effectiveness of these measures were also analyzed. To our knowledge, our paper is the first to point out the issue of privacy leakage of celebrity's ID card number caused by apps' real‐name authentication functions in China. We believe that our research will attract widespread attention from society regarding celebrity's privacy information protection.

A review of privacy-preserving research on federated graph neural networks

Graph neural networks are widely employed in diverse domains; however, they confront the peril of privacy infringement. To address this concern, federated learning emerges as a privacy-preserving approach that avoids sharing data for model training, effectively resolving the issue of privacy leakage in graph neural networks. The rapid advancement of federated neural networks has spurred the demand for more potent tools to enhance model performance owing to the concealed correlation information amongst federated learning participants. However, the structural attributes of federated graph neural networks render them vulnerable to inference attacks, reconstruction attacks, inversion attacks, and the like, potentially endangering privacy. This study delves into the intricacies of privacy-preserving within federated graph neural networks. Firstly, it introduces the architecture and variants of federated graph neural networks, analyzes the privacy risks encountered by these networks from four perspectives, and elucidates three primary attack methods. In accordance with the privacy-preserving mechanism of federated graph neural networks, it summarizes the privacy-preserving techniques and synthesizes the existing strategies from four perspectives: encryption methods, perturbation methods, anonymization, and hybrid methods. Furthermore, it summarily presents the prevailing framework for preserving privacy in neural networks. Ultimately, this paper examines the challenges and outlines future research directions pertaining to federated graph neural network technology.

Reducing privacy risks of China's healthcare big data through the policy framework.

While healthcare big data brings great opportunities and convenience to the healthcare industry, it also inevitably raises the issue of privacy leakage. Nowadays, the whole world is facing the security threat of healthcare big data, for which a sound policy framework can help reduce privacy risks of healthcare big data. In recent years, the Chinese government and industry self-regulatory organizations have issued a series of policy documents to reduce privacy risks of healthcare big data. However, China's policy framework suffers from the drawbacks of the mismatched operational model, the inappropriate operational method, and the poorly actionable operational content. Based on the experiences of the European Union, Australia, the United States, and other extra-territorial regions, strategies are proposed for China to amend the operational model of the policy framework, improve the operational method of the policy framework, and enhance the operability of the operational content of the policy framework. This study enriches the research on China's policy framework to reduce privacy risks of healthcare big data and provides some inspiration for other countries.

Toward a Privacy-Preserving Face Recognition System: A Survey of Leakages and Solutions

Abstract Recent advancements in face recognition (FR) technology in surveillance systems make it possible to monitor a person as they move around. FR gathers a lot of information depending on the quantity and data sources. The most severe privacy concern with FR technology is its use to identify people in real-time public monitoring applications or via an aggregation of datasets without their consent. Due to the importance of private data leakage in the FR environment, academia and business have given it a lot of attention, leading to the creation of several research initiatives meant to solve the corresponding challenges. As a result, this study aims to look at privacy-preserving face recognition (PPFR) methods. We propose a detailed and systematic study of the PPFR based on our suggested six-level framework. Along with all the levels, more emphasis is given to the processing of face images as it is more crucial for FR technology. We explore the privacy leakage issues and offer an up-to-date and thorough summary of current research trends in the FR system from six perspectives. We also encourage additional research initiatives in this promising area for further investigation.

Robust quantum federated learning with noise

Federated learning is a decentralized machine learning approach where multiple participants collaboratively train machine learning models. With the development of quantum computing, there has been significant potential in the integration of quantum computing and federated learning. However, existing research has demonstrated that, similar to classical federated learning models, quantum federated learning models also face various security threats and privacy leakage issues. This paper proposes a quantum federated learning model based on quantum noise. Adding quantum noise to the model not only addresses privacy leakage, but also enhances the model robustness, effectively resists adversarial attacks. Specifically, extensive numerical simulations are conducted using various datasets to evaluate the effectiveness of the proposed method. The results reveal a more pronounced variation in robust training in high-dimensional datasets compared to low-dimensional datasets. Furthermore, the impact of noise intensity on model robustness is explored. Experimental demonstrate that a small amount of quantum noise does not have a significant impact on accuracy, and as the noise increases, the robustness of the model also improves. Finally, three different types of quantum noise were used for robustness testing in the paper to analyze the impact of quantum noise on the robustness of quantum machine learning models. The abundant experimental results have verified that the noise can improve the security of distributed quantum machine learning.

Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding

Hierarchy is an important and commonly observed topological property in real-world graphs that indicate the relationships between supervisors and subordinates or the organizational behavior of human groups. As hierarchy is introduced as a new inductive bias into the Graph Neural Networks (GNNs) in various tasks, it implies latent topological relations for attackers to improve their inference attack performance, leading to serious privacy leakage issues. In addition, existing privacy-preserving frameworks suffer from reduced protection ability in hierarchical propagation due to the deficiency of adaptive upper-bound estimation of the hierarchical perturbation boundary. It is of great urgency to effectively leverage the hierarchical property of data while satisfying privacy guarantees. To solve the problem, we propose the Poincar\'e Differential Privacy framework, named PoinDP, to protect the hierarchy-aware graph embedding based on hyperbolic geometry. Specifically, PoinDP first learns the hierarchy weights for each entity based on the Poincar\'e model in hyperbolic space. Then, the Personalized Hierarchy-aware Sensitivity is designed to measure the sensitivity of the hierarchical structure and adaptively allocate the privacy protection strength. Besides, Hyperbolic Gaussian Mechanism (HGM) is proposed to extend the Gaussian mechanism in Euclidean space to hyperbolic space to realize random perturbations that satisfy differential privacy under the hyperbolic space metric. Extensive experiment results on five real-world datasets demonstrate the proposed PoinDP’s advantages of effective privacy protection while maintaining good performance on the node classification task.

A Secure Face Verification Scheme Based on Fully Homomorphic Encryption with Anonymity

With the widespread adoption of cloud computing, the face verification process often requires the client to upload the face to an untrusted cloud server to obtain the verification results. Privacy leakage issues may arise if the client’s private information is not protected. This paper proposes a secure and anonymous face verification scheme using fully homomorphic encryption technology and SealPIR. Our scheme is a three-party solution that requires a third-party server trusted by the client. This scheme not only prevents the client’s facial data from being obtained by untrusted data servers but also prevents the data server from learning the index corresponding to the face that the client wants to verify. In a single-face verification process, the client only needs to perform one upload operation and one download operation, with a communication volume of 264 KB. We can complete a privacy-protected anonymous face verification process in 84.91 ms.

Research on Privacy Protection in Federated Learning Combining Distillation Defense and Blockchain

Traditional federated learning addresses the data security issues arising from the need to centralize client datasets on a central server for model training. However, this approach still poses privacy protection risks. For instance, central servers cannot verify privacy leaks resulting from poisoning attacks by malicious clients. Additionally, adversarial sample attacks can infer specific samples from the original data by testing the local models on client devices. This paper proposes a federated learning privacy protection method combining distillation defense technology with blockchain architecture. The method utilizes distillation defense technology to reduce the sensitivity of client devices participating in federated learning to perturbations and enhance their ability to resist adversarial sample attacks locally. This not only reduces communication overhead and improves learning efficiency but also enhances the model’s generalization ability. Furthermore, the method leverages the “decentralized” nature of blockchain architecture as a trusted record-keeping mechanism to audit information interactions among clients and shared model parameters. This addresses privacy leakage issues resulting from poisoning attacks by some clients during the model construction process. Simulation experiment results demonstrate that the proposed method, compared with traditional federated learning, ensures model convergence, detects malicious clients, and improves the participation level of highly reputable clients. Moreover, by reducing the sensitivity of local clients to perturbations, it enhances their ability to effectively resist adversarial sample attacks.

An Anonymous and Supervisory Cross-chain Privacy Protection Protocol for Zero-trust IoT Application

Internet of things (IoT) development tends to reduce the reliance on centralized servers. The zero-trust distributed system combined with blockchain technology has become a hot topic in IoT research. However, distribution data storage services and different blockchain protocols make network interoperability and cross-platform more complex. Relay chain is a promising cross-chain technology that solves the complexity and compatibility issues associated with blockchain cross-chain transactions by utilizing relay blockchains as cross-chain connectors. Yet relay chain cross-chain transactions need to collect asset information and implement asset transactions via two-way peg. Due to the release of user transaction information, there is the issue of privacy leakage. In this article, we propose a cross-chain privacy protection protocol based on the Groth16 zero-knowledge proof algorithm and coin-mixing technology, which changes the authentication mechanism and uses a combination of generating functions to map virtual external addresses in transactions. It allows fast cross-chain anonymous transactions while hiding the genuine user’s address. The experiment shows that, in a zero-trust IoT context, our scheme can effectively protect user privacy information, accomplish controlled transaction traceability operations, and guarantee cross-chain transaction security.

FPCNN: A fast privacy-preserving outsourced convolutional neural network with low-bandwidth

Convolutional neural networks (CNNs) have been widely employed in image and video recognition tasks due to their superior performance. With the increasing popularity of machine learning as a service, CNN model owners often outsource their models to the cloud to provide inference services. However, the user data and outsourced models processed by cloud-based CNN inference applications suffer from privacy leakage issues. Therefore, several studies have proposed different privacy-preserving CNN architectures. Most of these approaches are based on heavyweight cryptographic methods, which require significant computational resources. In this study, we propose a novel scheme, a Fast Privacy-Preserving Outsourced Convolutional Neural Network with Low Bandwidth (FPCNN), to provide secure inference services on the cloud using outsourced data and models. Specifically, for linear CNN layers, FPCNN significantly reduces the bandwidth requirements between servers by applying scaled noise models and scaled secret sharing. For nonlinear CNN layers, we first optimize the multiplication operation under secret sharing to save bandwidth. Subsequently, based on the nonlinear input characteristics of CNNs, we introduce a secure parallel rectified linear unit (ReLU) computation protocol that significantly reduces the number of communication rounds. Finally, the experimental results demonstrate that FPCNN achieves a speedup of 2.66× and a 32.8× reduction in bandwidth compared with state-of-the-art methods.

Privacy-preserving offloading scheme in multi-access mobile edge computing based on MADRL

With the development of industrialization and intelligence, the Industrial Internet of Things (IIoT) has gradually become the direction for traditional industries to transform into modern ones. In order to adapt to the emergence of a large number of edge access devices such as sensors, as well as the demand for high-consumption and low-latency computing tasks, Mobile Edge Computing (MEC) has been proposed as an effective paradigm by the academic community. Users can offload tasks to MEC servers, greatly reducing the computing latency and energy consumption when using services. However, traditional single access point edge computing networks cannot meet the usage requirements of a large number of users. In addition, the privacy leakage issues arising from the offloading process are also easily overlooked. In this paper, we propose a privacy-preserving offloading scheme based on stochastic game theory considering multiple access points. We first construct a multi-access point offloading framework and quality of service (QoS) model. In terms of privacy, the privacy risks caused by the offloading preferences of different edge nodes are studied, and the privacy entropy is used to evaluate the privacy protection level. We comprehensively consider the energy consumption, latency requirements, user experience, and privacy protection of the system and formulate the problem as a Markov Decision Process (MDP). Finally, a joint optimal DRL algorithm with privacy preservation (JODRL-PP) is proposed to achieve the optimal offloading scheme of the system. Simulation results verify the effectiveness of our model and algorithm.

Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential Privacy

With the great amount of available data, especially collecting from the ubiquitous Internet of Things (IoT), the issue of privacy leakage arises increasingly concerns recently. To preserve the privacy of IoT datasets, traditional methods usually calibrate random noises on the data values to achieve differential privacy (DP). However, the amount of the calibrating noises should be carefully designed and a heedless value will definitely degrade the availability of datasets. Thus, in this work, we propose a stochastic perturbation method to sanitize the dataset, where the perturbation is obtained from the rest samples in the same dataset. In addition, we derive the expression of the utility level based on its unique framework and prove that the proposed algorithm can achieve the <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -DP. To show the effectiveness of the proposed algorithm, we conduct extensive experiments on real-life datasets by various functions, such as query answers and machine learning tasks. By comparing with the state-of-the-art methods, our proposed algorithm can achieve a better performance under the same privacy level.

A federated collaborative recommendation model for privacy-preserving distributed recommender applications based on microservice framework

The wide adoption of IoT technologies has accelerated the accumulation of big data. Recommender systems (RS) is one of the most effective methods to extract user interested items from the huge volume of big data. However, implementing a recommender system over the distributed error-prone IoT devices faces two challenges. On the one hand, the distributed IoT devices may randomly fail to deliver its local data due to hardware malfunction, which may cause unavailability of the recommender system. Moreover, collecting the raw data from the distributed IoT devices may cause data privacy leakage issue, since the privacy data of user-item interaction records may be abused by vicious parties. In view of these challenges, we propose a federated collaborative recommendation model based on microservice framework in this paper to implement privacy preserving distributed recommendation applications. Firstly, we utilize the federated learning framework to train the collaborative recommendation model, where the raw data on each distributed device is kept locally and only item related model parameters are exposed to train the federated recommendation model. Moreover, we adopt the microservice framework to encapsulate different functions of the federated recommender model. Each distributed device can participate in the federated training process via service registration and service discovery function of the microservice framework. Furthermore, we enhance the typical Neural Collaborative Filtering model with the proposed FedNeuMF model by fusing auxiliary user profiles and item attributes to improve the recommendation accuracy. Finally, we conduct a set of experiments on three real-world datasets to check the performance of our proposal.

An IoT and Wearables-Based Smart Home for ALS Patients

In recent years, assistive wearables technologies based on Internet of Things (IoT) platforms for amyotrophic lateral sclerosis (ALS) patients trigger broad interests. Nevertheless, the user privacy leakage issue, owing to the scene camera installed on wearables to analyze environmental information, hinders further success use for ALS patients. To address this issue, in this article, a smart human-environment interactive (HEI) environment, including eye motion detection, radio-frequency identification (RFID), and speech feedback techniques, under the IoT framework is presented. Here, the users’ intentions are first interpreted by the eye motion classification, and then the target smart devices are reported and desired operations are confirmed by the RFID and speech feedback system in a hand-shaking manner. A high average accuracy of 93.2% is experimentally achieved, demonstrating the feasibility of the proposed method in obtaining satisfying performance while avoiding potential privacy leakage.

A Fast Method for Protecting Users’ Privacy in Image Hash Retrieval System

Effective search engines based on deep neural networks (DNNs) can be used to search for many images, as is the case with the Google Images search engine. However, the illegal use of search engines can lead to serious compromises of privacy. Affected by various factors such as economic interests and service providers, hackers and other malicious parties can steal and tamper with the image data uploaded by users, causing privacy leakage issues in image hash retrieval. Previous work has exploited the adversarial attack to protect the user’s privacy with an approximation strategy in the white-box setting, although this method leads to slow convergence. In this study, we utilized the penalty norm, which sets a strict constraint to quantify the feature of a query image into binary code via the non-convex optimization process. Moreover, we exploited the forward–backward strategy to solve the vanishing gradient caused by the quantization function. We evaluated our method on two widely used datasets and show an attractive performance with high convergence speed. Moreover, compared with other image privacy protection methods, our method shows the best performance in terms of privacy protection and image quality.

Attention-based federated incremental learning for traffic classification in the Internet of Things

The Internet of Things (IoT) traffic follows non-independent and identical distribution (non-IID). Traditional machine learning classification methods will cause low classification accuracy, high communication costs, and privacy leakage issues. Federated Learning enables several clients to train a deep learning model collaboratively without requiring any of the clients to share their local data with a centralized server. In this paper, we propose a novel attention-based federated incremental learning algorithm: Fed-SOINN. We introduce the attention mechanism to improve the weight of parameters uploaded by clients which are beneficial to the global model, where instead of the full gradient, only a small subset of important gradients is communicated. Meanwhile, we improve the sparsity of the model by upgrading the online optimization function in Fed-SOINN, it also brings faster convergence speed and higher accuracy in the changeable network environment. Results reveal that Fed-SOINN has improved detection accuracy by 3.1% compared with benchmark methods and can reduce the number of communications rounds up to 73%. When facing new traffic categories, the incremental learning mechanism in Fed-SOINN also effectively identify unknown traffic categories.