Awareness Of Privacy Protection Research Articles

DDP-FedFV: A Dual-Decoupling Personalized Federated Learning Framework for Finger Vein Recognition

Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, a distributed training method that protects data privacy without sharing data across endpoints, is gradually being promoted and applied. Nevertheless, its performance is severely limited by heterogeneity among datasets. To address these issues, this paper proposes a dual-decoupling personalized federated learning framework for finger vein recognition (DDP-FedFV). The DDP-FedFV method combines generalization and personalization. In the first stage, the DDP-FedFV method implements a dual-decoupling mechanism involving model and feature decoupling to optimize feature representations and enhance the generalizability of the global model. In the second stage, the DDP-FedFV method implements a personalized weight aggregation method, federated personalization weight ratio reduction (FedPWRR), to optimize the parameter aggregation process based on data distribution information, thereby enhancing the personalization of the client models. To evaluate the performance of the DDP-FedFV method, theoretical analyses and experiments were conducted based on six public finger vein datasets. The experimental results indicate that the proposed algorithm outperforms centralized training models without increasing communication costs or privacy leakage risks.

Efficient suppression algorithms for preserving trajectory privacy

Concerns about the security of trajectory data have surfaced amid rising public awareness of privacy protection. In this study, we focus on how to protect personal information effectively when adversaries have access to a portion of users’ trajectory data. To resolve this issue, we design a graph-based index structure to store users’ trajectory data and develop a novel subtrajectory upper bound estimation method and corresponding pruning strategies. Using a graph-based index structure and pruning strategies, we propose a global suppression algorithm and a local suppression algorithm to prevent personal information from being extracted from the original trajectory data. Experimental results show that the maintenance cost of the graph-based index structure is low when performing global and local suppression, and that the pruning strategies effectively eliminate unnecessary computation of non-upper-bound subtrajectories. Therefore, the execution times of the proposed algorithms are far shorter than those of existing algorithms.

Chaotic image encryption algorithm based on dynamic Hachimoji DNA coding and computing

With the increasing awareness of privacy protection, people pay more and more attention to strengthening the security of image data transmitted over the network. Therefore, this paper designs a chaotic image encrypting algorithm based on dynamic Hachimoji DNA coding and computing to protect images. The Hachimoji DNA coding method provides richer coding rules to dynamically encode images than the traditional DNA coding method, improving the complexity and security of the encryption algorithm. First, the original image is rearranged and encoded with the dynamic Hachimoji DNA coding method according to the sorting and encoding controller sequence generated by a six-dimensional hyperchaotic system. Second, various DNA operations are performed on the encoded image. Among these operations, we not only use the common operations but also propose a new DNA operation called bitwise inversion. Finally, the DNA image is decoded using the dynamic decoding method to obtain the encrypted image. Experiments demonstrated that the image encryption algorithm has a good security effect and can effectively resist common attacks.

Privacy-preserving federated discovery of DNA motifs with differential privacy

DNA sequence motif discovery is an important issue in gene research, which helps identify transcription factor binding sites in DNA sequences to reveal the mechanisms that regulate gene expression. However, the growing awareness of privacy protection and increasingly stringent regulations pose challenges to data collection and usage. This paper proposes DP-FLMD, a privacy-preserving federated framework for discovering DNA sequence motifs. We employ federated learning, allowing participants to store their raw data locally and upload only selected parameters to protect data privacy. Nevertheless, privacy concerns still persist, as these participant parameters may potentially compromise their privacy to some extent. To enhance privacy protection, we incorporate a differential privacy algorithm to add noise to these parameters. Additionally, we verify that the method satisfies ϵ-differential privacy through theoretical analysis. Extensive experiments are performed on six datasets, demonstrating that DP-FLMD achieves a good trade-off between privacy and utility. Besides, we investigate the effect of parameters on DP-FLMD. In the future, our research will focus on exploring enhanced privacy protection mechanisms, expanding the scope of framework applications, and further optimizing the trade-off between privacy and utility.

Self-Supervised On-Device Federated Learning From Unlabeled Streams

The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously improve accuracy. Self-supervised representation learning has achieved promising performances using centralized unlabeled data. However, the increasing awareness of privacy protection limits centralizing the distributed unlabeled image data on edge devices. While federated learning has been widely adopted to enable distributed machine learning with privacy preservation, without a data selection method to efficiently select streaming data, the traditional federated learning framework fails to handle these huge amounts of decentralized unlabeled data with limited storage resources on edge. To address these challenges, we propose a Self-supervised On-device Federated learning framework with coreset selection, which we call SOFed, to automatically select a coreset that consists of the most representative samples into the replay buffer on each device. It preserves data privacy as each client does not share raw data while learning good visual representations. Experiments demonstrate the effectiveness and significance of the proposed method in visual representation learning.

Federated Latent Dirichlet Allocation for User Preference Mining

In the field of Web services computing, a recent demand trend is to mine user preferences based on user requirements when creating Web service compositions, in order to meet comprehensive and ever evolving user needs. Machine learning methods such as the latent Dirichlet allocation (LDA) have been applied for user preference mining. However, training a high-quality LDA model typically requires large amounts of data. With the prevalence of government regulations and laws and the enhancement of people’s awareness of privacy protection, the traditional way of collecting user data on a central server is no longer applicable. Therefore, it is necessary to design a privacy-preserving method to train an LDA model without massive collecting or leaking data. In this paper, we present novel federated LDA techniques to learn user preferences in the Web service ecosystem. On the basis of a user-level distributed LDA algorithm, we establish two federated LDA models in charge of two-layer training scenarios: a centralized synchronous federated LDA (CSFed-LDA) for synchronous scenarios and a decentralized asynchronous federated LDA (DAFed-LDA) for asynchronous ones. In the former CSFed-LDA model, an importance-based partially homomorphic encryption (IPHE) technique is developed to protect privacy in an efficient manner. In the latter DAFed-LDA model, blockchain technology is incorporated and a multi-channel-based authority control scheme (MCACS) is designed to enhance data security. Extensive experiments over a real-world dataset ProgrammableWeb.com have demonstrated the model performance, security assurance and training speed of our approach.

Research on Residents' Willingness to Protect Privacy in the Context of the Personal Information Protection Law: A Survey Based on Foshan Residents' Data

The Personal Information Protection Law, as the frst law on personal information protection in China, hits the people’s most concerned, realistic and direct privacy and information security issues, and plays an extremely important role in promoting the development of the digital economy, the legalization of socialism with Chinese characteristics and social public security, and marks a new historical development stage in the protection of personal information in China. However, the awareness of privacy protection and privacy protection behavior of the public in personal information privacy protection is weak. Based on the literature review and in-depth understanding of current legal regulations, this study integrates the relevant literature and theoretical knowledge of the Personal Protection Law to construct a conceptual model of “privacy information protection willingness—privacy information protection behavior”. Taking the residents of Foshan City as an example, this paper conducts a questionnaire survey on their attitudes toward the Personal Protection Law, analyzes the factors infuencing their willingness to protect their privacy and their behaviors, and explores the mechanisms of their infuencing variables, to provide advice and suggestions for promoting the protection of privacy information and building a security barrier for the high-quality development of public information security.

Defending Privacy Inference Attacks to Federated Learning for Intelligent IoT with Parameter Compression

Federated learning has been popularly studied with people’s increasing awareness of privacy protection. It solves the problem of privacy leakage by its ability that allows many clients to train a collaborative model without uploading local data collected by Internet of Things (IoT) devices. However, there are still threats of privacy leakage in federated learning. The privacy inference attacks can reconstruct the privacy data of other clients based on GAN from the parameters in the process of iterations for global models. In this work, we are motivated to prevent GAN-based privacy inference attacks in federated learning. Inspired by the idea of gradient compression, we propose a defense method called Federated Learning Parameter Compression (FLPC) which can reduce the sharing of information for privacy protection. It prevents attackers from recovering the private information of victims while maintaining the accuracy of the global model. Extensive experimental results demonstrated that our method is effective in the prevention of GAN-based privacy inferring attacks. In addition, based on the experimental results, we propose a norm-based metric to assess the performance of privacy-preserving.

MVPN: A Defense Architecture against VPN Traffic Hijacking Based on MTD

With the increasing awareness of privacy protection, Virtual Private Networks (VPNs) are widely used to build a more secure communication tunnel. However, a traffic hijacking attack called blind in/on-path has seriously threatened the security of VPNs. Inspired by Moving Target Defense (MTD), Moving VPN architecture (MVPN) is designed to defend against such attacks. MVPN includes multiple nodes to encrypt and decrypt traffic to enhance reliability. Thus, the consistency judgment algorithm is proposed to make MVPN obtain the ability to perceive attacks. Moreover, according to the judgment result and the state update strategy, the MVPN state is dynamically changed so as to achieve the purpose of active defense. In addition, this paper also designs the multichannel packet classification mechanism and availability assurance strategy, which not only ensures the security and availability of the system but also reduces the performance loss caused by the defense strategy. The simulation verifies that MVPN architecture can reduce the success rate of blind in/on-path attacks by five orders of magnitude. In addition, we implemented and deployed MVPN based on the fast-forwarding framework of the Data Plane Development Kit (DPDK). Experiments in the real environment also show that the MVPN system can effectively prevent attackers from carrying out blind in/on-path attacks.

SetRkNN: Efficient and Privacy-Preserving Set Reverse kNN Query in Cloud

The advance of cloud computing has driven a new paradigm of outsourcing large-scale data and data-driven services to public clouds. Due to the increased awareness of privacy protection, many studies have focused on addressing security and privacy issues in outsourced query services. Although many privacy-preserving schemes have been proposed for various query types, the set reverse k nearest neighbors (RkNN) query is still an unexplored area. Even if some existing schemes can be adapted to achieve privacy-preserving set RkNN queries, they will suffer from linear search efficiency. As a steppingstone, in this paper, we propose an efficient and privacy-preserving set RkNN query scheme over encrypted data with sublinear query efficiency. Specifically, we first design an inverted prefix index to organize the set dataset and propose an algorithm to traverse the index with sublinear search efficiency. Then, we propose two oblivious data comparison protocols based on a symmetric homomorphic encryption (SHE) scheme and design the private filter/refinement protocols to preserve the privacy of index searching. After that, we propose an access pattern privacy-preserving set RkNN query scheme by using private filter/refinement protocols. Rigorous security analysis demonstrates that our scheme can protect data privacy and access pattern privacy. Experimental results indicate that our scheme is more efficient than the available naive solution in terms of computational costs and communication overheads.

A federated feature selection algorithm based on particle swarm optimization under privacy protection

Feature selection is an important preprocessing technique in the fields of data mining and machine learning. With the promotion of privacy protection awareness, recently it becomes a very practical and challenging issue to select high-quality feature subsets while ensuring the privacy of all participants. However, there is a lack of research results on this issue, i.e., feature selection under privacy protection. Aiming at the issue, this paper proposes a federated feature selection framework for the first time. In the framework, inspiring by the idea of federated learning, a credible third participant is introduced to process and integrate optimal feature subsets from multiple participants. On the basis of the framework, a federated evolutionary feature selection algorithm based on particle swarm optimization is proposed to effectively solve feature selection problems with multiple participants under privacy protection. Two new operators satisfying the requirement of privacy protection, i.e., the feature assembling strategy with multi-participant cooperation and the swarm initialization strategy guided by assembling solution, are designed to improve the ability of the proposed algorithm. Compared with several typical assembling feature selection algorithms on 15 data sets, experimental results show that the proposed algorithm can significantly improve the classification accuracy of the feature subset selected by each participant, while protecting the privacy of data.

Health Records Database and Inherent Security Concerns: A Review of the Literature.

The use of electronic health records (EHRs) has grown significantly in the past decade. Health information databases contain sensitive patient information, including their names and addresses, tests, diagnoses, treatment, and medical history. This information should be secured and protected from manipulation and fraudulent use by third parties. EHRs are expected to increase efficiency in healthcare delivery, improve healthcare quality, and relieve increased financial pressure. Despite these expected benefits, EHRs are potentially vulnerable to security concerns that may affect the confidentiality and privacy of patients' personal information. This paper presents a literature review of EHRs, factors that support the security and safety of health records, potential security breaches, and solutions to inherent security concerns. The study collects data through a systematic review of past studies that have addressed the topic of EHRs and security issues, and other relevant publications on EHR systems, and procedures that help safeguard health records databases. A total of 30 sources are analyzed for all pertinent information regarding security concerns of health records databases. These sources were obtained through an internet search on credible databases, including Google Scholar, PubMed, and CINAHL databases. The results of the current study reveal the perceived vulnerability of EHRs to security concerns, common security issues, the nature of these common security concerns, Health Insurance Portability and Accountability Act rules, provider responsibilities, and recommendations for reducing EHR security risks. This paper also reveals effective strategies such as privacy-protection awareness and staff training to enhance the security of health records databases.

Promoting Information Privacy Protection Awareness for Internet of Things (IoT)

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

Multi-Source Selection Transfer Learning with Privacy-Preserving

Transfer learning has ability to create learning task of weakly labeled or unlabeled target domain by using knowledge of source domain to help, which can effectively improve the performance of target learning task. At present, the increased awareness of privacy protection restricts access to data sources and poses new challenges to the development of transfer learning. However, the research on privacy protection in transfer learning is very rare. The existing work mainly uses differential privacy technology and does not consider the distribution difference between data sources, or does not consider the conditional probability distribution of data, which causes negative transfer to harm the effect of algorithm. Therefore, this paper proposes multi-source selection transfer learning algorithm with privacy-preserving MultiSTLP, which is used in scenarios where target domain contains unlabeled data sets with only a small amount of group probability information and multiple source domains with a large number of labeled data sets. Group probability means that the class label of each sample in target data set is unknown, but the probability of each class in a given data group is available, and multiple source domains indicate that there are more than two source domains. The number of data set contains more than two data sets of source domain and one data set of target domain. The algorithm adapts to the marginal probability distribution and conditional probability distribution differences between domains, and can protect the privacy of target data and improve classification accuracy by fusing the idea of multi-source transfer learning and group probability into support vector machine. At the same time, it can select the representative dataset in source domains to improve efficiency relied on speeding up the training process of algorithm. Experimental results on several real datasets show the effectiveness of MultiSTLP, and it also has some advantages compared with the state-of-the-art transfer learning algorithm.

Comprehensive Assessment of Mobile Service Privacy Security Based on FAHP and D–S Theory

As an indispensable medium for people’s daily life and communication, mobile applications provide users with a variety of services. In order to enjoy these services, users inevitably need to provide personal privacy information or authorization to application providers in the process of use. In the case of insufficient privacy security evaluation, even if users use the applications verified by the application market, their privacy security will be threatened because of their weak awareness of privacy protection. Therefore, in order to ensure the privacy security of users, this paper establishes a mobile service assessment attribute model including 3 risk categories and 11 risk indicators. Based on this model, this paper proposes a service risk weight assessment method based on FAHP (fuzzy analytical hierarchy process), defines the risk level and its trust degree from two aspects of risk frequency and risk loss, and puts forward a reasonable fusion method for different assessment results of risk level based on D–S (Dempster–Shafer) theory, so as to realize the multidimensional and multilayer assessment of mobile service risk. Finally, the case analysis shows that the method proposed in this paper is reasonable and feasible and has important value for improving the security of mobile services and managing users’ privacy information.

MATEC: A lightweight neural network for online encrypted traffic classification

Increased awareness of privacy protection has led to a surge in the volume of encrypted traffic, which creates a heavy burden for efficient network management (e.g. quality-of-service guarantees). The opacity of encrypted traffic essentially requires high computational overheads to make traffic classification, which is even worse when encrypted traffic surges. However, existing deep learning approaches sacrifice the efficiency to obtain high-precision classification results, which are no longer suitable for scenarios with large volumes of encrypted traffic. In this paper, a lightweight and online approach implemented as MATEC is proposed. The way we optimize the classification process follows the “Maximizing the reuse of thin modules” design principle. The multi-head attention and the convolutional network are adopted in the thin module. Attributed to the one-step interaction of all packets and the parallel computing of the multi-head attention mechanism, a key advantage of our model is that the number of parameters and running time are significantly reduced. In addition, the effectiveness and efficiency of convolutional networks have been proved in traffic classification. Comparisons to the existing state-of-the-art models on three typical datasets demonstrate that the proposed MATEC model has higher accuracy and running efficiency. In addition, the number of parameters is reduced to 1.8% of the state-of-the-art models and the training time halves.

Inferring FOLLOW Relationship from Repost Relationship between Users on Sina Weibo

In recent years, online social platforms such as sina weibo have been playing an increasingly important role in our lives. With the improvement of public awareness of privacy protection, however, it is increasingly difficult to obtain exhaustive direct data of FOLLOW relationship between users, which restricts the analysis and research on real online social networks. In the context of incomplete disclosure of information, we have to consider inferring from limited public data to more unknown information before further analysis and research with network topology. To overcome this obstacle, we try to recover the FOLLOW relationship by repost relationship. We collect repost data of six different bloggers on sina weibo to generate six independent networks, and propose an effective method to recover FOLLOW relationship between users. Our method is superior to other existing methods, and helps us draw a conclusion that two users establishing an indirect repost relationship with other two users, compared to other indirect relationship, are of greater possibility to maintain the FOLLOW relationship. At the same time, we also extend our method to greatly improve the recovering accuracy.

A distributed Peer-to-Peer energy transaction method for diversified prosumers in Urban Community Microgrid System

As massive integration of Distributed Energy Resources (DERs), the role of end-users in the Urban Community Microgrid System (UCMS) has transformed from traditional consumers into prosumers with capabilities of both energy production and consumption. The exchange of energy between autonomous microgrid prosumers can be achieved with the introduction of Peer-to-Peer (P2P) energy transaction, promoting the efficient allocation of energy in the UCMS. However, the existing centralized P2P energy transaction approaches require microgrid transaction brokers to obtain prosumers’ private data, including energy resource configuration, operation status, and energy production/consumption schedule. With the enhancement of prosumers’ awareness of privacy protection, it will be increasingly more difficult for the brokers to obtain such private data in practical application scenarios, resulting in obstacles on the implementation of such centralized approach. Thus, a novel distributed P2P energy transaction method based on the double auction market is proposed in this paper. Prosumers first generate the information of energy supply and demand autonomously utilizing distributed energy management model, then set the price targeting profit maximization, and finally initiate P2P energy transaction mutually in the double auction energy market. Compared with the existing centralized approaches, the method proposed in this paper can achieve the coordination and complementarity of energy in the UCMS, promoting economic benefit, energy self-sufficiency, and renewable energy self-consumption without sacrificing privacy preservation and robustness.

Location Semantics Identification via Users’ Clickstreams in Mobile Social Networking

With the rise of social networking and the awareness of privacy protection, the large-scale positioning data is getting harder to access while the clickstream data of social networking is accumulating. It is necessary to conduct a comprehensive study on the temporal property and spatial distribution of social network’s clickstream to determine the latent relationship between clickstream and location semantics which semantically annotate locations to help us understand the purpose of a social network user at a certain location and a particular time. In this paper, we first infer the location semantics from Internet logs as our baseline which is a large scale, imprecise positioning mobile dataset through combining the utilization of derived time pattern and speed pattern. Then we design a method to reconstruct location semantics through the social network. We are the first to our knowledge that define “click motif” of social network’s clickstream which is the combination of clicks in clickstream that occurs repeatedly and has significance. Click motif not only can help us to classify location semantics but also enhance the user experience of social networking. Besides, we propose a completed process to extract typical click motifs from a large-scale complicated clickstream data via embedding and clustering method. By extensive experiments and analysis, we demonstrate that different combination of click motifs can convey the users’ location semantics, therefore clickstream may disclose location privacy.

User Behavior Clustering Scheme With Automatic Tagging Over Encrypted Data

User behavior clustering analysis has a wide range of applications in business intelligence, information retrieval, and image pattern recognition and fault diagnosis. Most of existing methods of user behavior have some problems such as weak generality and the lack of tags of clustering. With the increasing awareness of privacy protection, user behavior analysis also needs to support for ciphertext to protect user data. Based on clustering algorithm, homomorphic encryption technology and information security, in this paper, we propose a user behavior clustering scheme that supports automatic tags on ciphertext. Firstly, design a security protocol corresponding to the basic operations such as addition, multiplication and comparison and apply to the scheme. Then, the relevant features of the user behavior are merged with the clustering process, the latent factor model, and matrix decomposition. We have implemented our method and evaluated its performance using K-means and K-means++ clustering. The results show that the scheme can auto tags over encrypted data, and the tag also meets the actual situation, which proves the validity and generality of the scheme.