Privacy Of User Data Research Articles

A DDPG-based energy efficient federated learning algorithm with SWIPT and MC-NOMA

Federated learning (FL) has emerged as a promising distributed machine learning technique. It has the potential to play a key role in future Internet of Things (IoT) networks by ensuring the security and privacy of user data combined with efficient utilization of communication resources. This paper addresses the challenge of maximizing energy efficiency in FL systems. We employed simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) techniques. Also, we jointly optimized power allocation and central processing unit (CPU) resource allocation to minimize latency-constrained energy consumption. We formulated an optimization problem using a Markov decision process (MDP) and utilized a deep deterministic policy gradient (DDPG) reinforcement learning algorithm to solve our MDP problem. We tested the proposed algorithm through extensive simulations and confirmed it converges in a stable manner and provides enhanced energy efficiency compared to conventional schemes.

Perlindungan Hukum Terhadap Pengguna Aplikasi E-Wallet Dana

Introduction: It is becoming easier for people to use the internet, which previously had to be done in an internet cafe, but now it is quite practical to use a smartphone/Android. This opportunity is attractive for funding application service providers to develop their business in various ways. The impact is that people's hedonism as consumers increases while the risk of problems increases.
 Purposes of the Research: To determine legal protection for consumers for users of the DANA E-Wallet application
 Methods of the Research: This research is normative legal research, to find legal norms related to consumer protection for the use of the DANA E-Wallet, while the data source used is secondary data.
 Results of the Research: There are still difficulties in obtaining accountability from DANA if losses occur to consumers. The government has taken preventive action by also issuing laws regulating electronic transactions. The security of personal data (privacy) of users must be protected from being the responsibility of the service provider, and if losses result from leaking of personal data, then the service provider can be asked for compensation.

Security in Mobile Network: Issues, Challenges and Solutions

INTRODUCTION: Mobile devices are integrated into daily activities of people's life. Compared to desktop computers the growth of mobile devices is tremendous in recent years. The growth of mobile devices opens vast scope for attackers on these devices.OBJECTIVES: This paper presents a deep study of different types of security risks involved in mobile devices and mobile applications. METHODS: In this paper we study various mechanisms of security risks for the mobile devices and their applications. We also study how to prevent these security risks in mobile devices.RESULTS: Various solutions are provided in paper through which operators can protect the security and privacy of user data and keep their customers' trust by implementing these procedures.CONCLUSION: This paper concludes with their solutions for providing a secure mobile network. This paper is structured as follows. Section 2 contains related work. Section 3 describes security problems. Section 4 discusses defensive methods and Section 5 gives the conclusion.

Sentiment Analysis of User Preference for Old Vs New Fintech Technology Using SVM and NB Algorithms

Abstract The aim of this study is to use sentiment analysis to compare the efficiency of old and new fintech technologies by collecting data from various sources and analyzing it using the SVM and NB algorithms. The study seeks to identify opinions or feelings from text in order to provide a clear picture of public opinion and the direction of the debate regarding old and new fintech technologies. The results of the study show that the SVM algorithm has an average accuracy of 87.32% and the NB algorithm has an average accuracy of 81.56% in testing the sample data in a comparison of old and new fintech technology on the internet. The study tested data in a comparison of two specific arguments, namely the debate about which technology is more efficient in old and new fintech on the internet. Despite many unresolved arguments, the study successfully proved that new fintech is more preferred than old fintech, with 71% positive sentiment directed towards new fintech. However, the dataset also found that 62% negative sentiment is directed towards new fintech, indicating that although new fintech is more preferred, there are still some issues that need to be addressed. One reason for negative sentiment towards new fintech may be the continued concerns about security and privacy of user data. Furthermore, other factors that may cause negative sentiment towards new fintech include a lack of understanding about how the technology works.

Multi-Slice Privacy-Aware Traffic Forecasting at RAN Level: A Scalable Federated-Learning Approach

Next-generation mobile networks are expected to meet the requirements of a wide range of new vertical services. Hence, the network slicing concept has been introduced, in which Mobile Virtual Network Operators (MVNOs) are allowed to provide various types of services over the same physical infrastructure, owned by an Infrastructure Provider (InP). To cope with an ever-changing traffic demand, MVNOs seek to pre-allocate/reconfigure the resources at the base stations in an anticipatory manner, based on traffic demand predictions. Ideally, conducting per-slice traffic forecasting requires information that is likely to disclose MVNO confidential information (i.e., business strategy or private user data). To secure data ownership while conducting traffic forecasting, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train their local models with their private dataset at each base station; subsequently, an associated InP global model can be updated through the aggregation of the local models. The results obtained by training the models on a real-world dataset indicate that the forecasting performance of our proposed approach is as accurate as state-of-the-art centralized solutions, while improving data privacy. To enable scalability, we further propose the Information-based Clustering FPLSTM (IC-FPLSTM) and Random Clustering FPLSTM (RC-FPLSTM) frameworks, dealing with large-scale cellular networks. These solutions demonstrate computation and communication cost efficiency significantly above the state-of-the-art.

The Impact of Metaverse on Graphic Design

This paper will look at how the metaverse is revolutionizing graphic design by enabling collaborative design processes, creating immersive virtual environments, and introducing unique challenges and opportunities for graphic designers. The metaverse revolutionizes graphic design through collaborative processes and immersive virtual environments, presenting unique challenges and opportunities for designers. Graphic designers can delve into the creation of immersive and interactive digital environments, design virtual avatars and 3D objects, and contribute to the development of visually stunning landscapes that will define the metaverse. They must adapt their skills and techniques to convey information and captivate users within this new medium effectively. In addition, graphic designers working in the metaverse must be mindful of ethical considerations related to inclusivity, accessibility, cultural sensitivity, and privacy. They should strive to create designs that are inclusive, accessible, and culturally sensitive, while also ensuring the privacy and security of user data. Overall, the metaverse presents graphic designers with new challenges and opportunities. They must continuously update their knowledge and skills to stay at the forefront of technological advancements in order to create compelling and impactful designs in the metaverse.

Research on Users’ Privacy-Sharing Intentions in the Health Data Tracking System Providing Personalized Services and Public Services

The utilization of user privacy data in the health data tracking system (HDTS) offers numerous benefits for businesses and public services, contingent upon users’ privacy sharing intentions. However, previous research neglected users’ preferences for public services and focused mainly on personalized services. Additionally, traditional privacy calculus theory has a limited focus on data security, leaving gaps in understanding individual and societal aspects. This study aims to fill these gaps by examining the influence of risk perception and factors like potential loss expectations, perceived personalized service benefits, group value identification, perceived public service utility, and perceived privacy on privacy sharing intentions in the context of personalized and public services. The results indicate a positive relationship between individual privacy protection perception and data sharing intention, as well as a positive relationship between group value identification and perceived public service utility with individuals’ privacy sharing intentions. Moreover, this research uncovers the moderating effect of information type sensitivity on the impact of perceived privacy and perceived public service utility on privacy sharing intentions, while there is no moderating effect of information type sensitivity on the relationship between group value identification and privacy sharing intentions. We recommend improving individual privacy education, ensuring data use transparency, and fostering identification with common group values to increase users’ privacy sharing intentions.

An empirical study on secure usage of mobile health apps: The attack simulation approach

: Mobile applications (apps) have proven their usefulness in enhancing service provisioning across a multitude of domains that range from smart healthcare, to mobile commerce, and areas of context-sensitive computing. In smart healthcare context, mobile health (mHealth) apps - representing a specific genre of mobile apps that manage health information - face some critical challenges relating to security and privacy of device and user data. In recent years, a number of empirically grounded, survey-based studies have been conducted to investigate secure usage of mHealth apps. However, such studies rely on self-reported behaviors documented via interviews or survey questions that lack practical approaches that can simulate attack scenario for monitoring users’ actions and behaviors while using mHealth apps. : Our objective was to conduct an empirical study - engaging participants with attack simulation scenarios and analyze their actions - for investigating the security awareness of mHealth app users. : We simulated some common security attack scenarios in mHealth context and engaged a total of 105 app users to monitor their actions and analyze their behavior. We analyzed users' data with statistical analysis including correlations test, descriptive analysis, and qualitative data analysis (i.e., thematic analysis method). : Our results indicate that whilst the minority of our participants perceived access permissions positively, the majority had negative views. Users provide their consent, granting permissions, without a careful review of privacy policies that leads to undesired or malicious access to health data. Findings also indicated that 73.3% of our participants had denied at least one access permission, and 36% of our participants preferred no authentication method. : The study complements existing research on secure usage of mHealth apps, simulates security threats to monitor users’ actions, and provides empirically grounded guidelines for secure development and usage of mobile health systems.

Blockchain Fog-based scheme for identity authentication in smart building

This paper presents a proposal for an authentication scheme for smart building systems and environments based on blockchain, its positive features, and fog computing. The most important feature that can be distinguished in the submitted proposal is its adoption of the principle of decentralization in contrast to traditional centralized documentation protocols, i.e. the proposed authentication system in which users and smart devices are implemented in a distributed and decentralized manner on the blockchain, that will provide a solution to a significant problem of low overall efficiency of the authentication process caused by a bottleneck in such important areas as computing capacity as well as centralized storage of a single authentication authority in the traditional model. There are also benefits from adding fog computing, and because it has higher computing and storage capabilities, it makes the data processing process more efficient, faster, more streamlined, and in line with the common necessities of the real-time IoT environment. The proposed scheme also provides solutions to protect the privacy of user data and increase the level of confidentiality, protection, and security, since a mysterious extractor was used to increase the confidentiality of the proposed model of the authentication system. Comparing a set of security schemes and conducting a security and performance analysis of the proposed scheme, the comparisons showed that the scheme can be characterized as having a good security level and an important efficiency level. The paper focused on specific aspects design of the authentication system, such as the registration and authentication process of all network entities, regardless of the specifics of the implementation of blockchain smart contracts.

A study of user data privacy protection algorithms in the context of metaverse based on emotional AI IoT

Abstract In the context of the metaverse, user data privacy protection has become an important issue. In this paper, firstly, a user data privacy leakage risk assessment scheme is designed by attribute sensitivity calculation, attribute similarity calculation, and attribute association calculation. Then a data privacy protection algorithm based on differential privacy is proposed, and the differential privacy data protection algorithm and implementation mechanism are described. Finally, the performance of the differential privacy protection algorithm is evaluated by analyzing the learning performance and protection performance of the algorithm. The results show that the learning performance of the differential privacy protection model decreases with increasing τ when q = 0 The larger q is, the better the protection performance of the model is, and the optimal τ value also shows a trend to the right. This study provides an effective method for user data privacy protection under the metaverse and offers new ideas for research in related fields.

The algorithm and model of user data privacy protection psychological anxiety

BackgroundWith the development of smart elderly care technology, the collection and use of user data caused privacy concerns and psychological anxiety. By studying algorithms and models that protect the privacy of user data, it is possible to prevent unauthorized access and misuse of data and promote the sustainable development of technology.Objects and MethodsSPSS23.0 was used to conduct a quantitative investigation and research, and the sample included 200 users using smart elderly care technology. The Stanford Privacy Concerns Questionnaire (PCQ) was used to assess the level of privacy anxiety among users. Algorithms and models, such as data encryption and blockchain technology, are used to discuss the privacy protection of user data.ResultsThe results showed that users had high psychological anxiety about data privacy protection in smart elderly care technology. According to the data, 70% of users expressed concerns about data breaches and privacy violations. However, after the analysis and application of the model and algorithm, the psychological anxiety of users on data privacy protection has been significantly reduced, and 50% of users are satisfied with the privacy protection measures taken.ConclusionsIn the context of smart elderly care, users’ psychological anxiety about data privacy protection exists. The introduction of new algorithms and models, such as data encryption and blockchain technology, can effectively reduce the psychological anxiety of users and improve the acceptance of data privacy protection measures.AcknowledgementThe Shaanxi Provincial Education Science 13th Five Year Plan (2020) project, No.SGH20Y1428.

Fedcs: Efficient communication scheduling in decentralized federated learning

Decentralized federated learning is a training approach that prioritizes user data privacy protection, while also offering improved scalability and robustness. However, as the number of edge devices participating in training increases, a significant communication overhead arises among devices located in different geographical locations. Therefore, designing a well-thought-out gradient synchronization strategy is crucial for minimizing the overall communication overhead of training.To tackle this issue, this article introduces a 2D-Ring network structure based parameter synchronization strategy and a 2D-attention-based device placement algorithm, aiming to minimize communication overhead. The parameter synchronization strategy devises a two-layer circular communication architecture for the devices involved in training, thereby reducing the overall frequency of parameter synchronization in decentralized federated learning. By taking into account the total communication overhead and the device placement strategy, an optimization problem is formulated. Specifically, a 2D-attention neural network is constructed to optimize the device placement solution based on 2D-Ring network structure, leading to reduced communication overhead. Moreover, an evaluation model is designed to assess the communication overhead in a complex decentralized system during federated training. This enables precise determination of the total communication overhead throughout the training process, providing valuable insights for devising the device placement strategy. Extensive simulations confirm that the proposed approach achieves a substantial reductions of 55% and 64% in the total communication overhead for decentralized federated learning training with 50 and 100 devices, respectively.

Deep Neural Network Quantization Framework for Effective Defense against Membership Inference Attacks.

Machine learning deployment on edge devices has faced challenges such as computational costs and privacy issues. Membership inference attack (MIA) refers to the attack where the adversary aims to infer whether a data sample belongs to the training set. In other words, user data privacy might be compromised by MIA from a well-trained model. Therefore, it is vital to have defense mechanisms in place to protect training data, especially in privacy-sensitive applications such as healthcare. This paper exploits the implications of quantization on privacy leakage and proposes a novel quantization method that enhances the resistance of a neural network against MIA. Recent studies have shown that model quantization leads to resistance against membership inference attacks. Existing quantization approaches primarily prioritize performance and energy efficiency; we propose a quantization framework with the main objective of boosting the resistance against membership inference attacks. Unlike conventional quantization methods whose primary objectives are compression or increased speed, our proposed quantization aims to provide defense against MIA. We evaluate the effectiveness of our methods on various popular benchmark datasets and model architectures. All popular evaluation metrics, including precision, recall, and F1-score, show improvement when compared to the full bitwidth model. For example, for ResNet on Cifar10, our experimental results show that our algorithm can reduce the attack accuracy of MIA by 14%, the true positive rate by 37%, and F1-score of members by 39% compared to the full bitwidth network. Here, reduction in true positive rate means the attacker will not be able to identify the training dataset members, which is the main goal of the MIA.

Privacy-preserving multi-party deep learning based on homomorphic proxy re-encryption

Deep learning is an important branch of artificial intelligence. However, the first task of deep learning is to collect data, which will seriously threaten user data privacy. The existing dual server privacy-preserving deep learning schemes operate under the assumption that two semi-honest servers will not collude, but this security assumption may be too robust. This paper proposes a privacy-preserving multiparty deep learning scheme based on a homomorphic proxy re-encryption scheme, which can resist collusion between the semi-honest servers. Introducing a fog node with high response and low latency characteristics as a proxy and leveraging a one-way homomorphic proxy re-encryption scheme to convert the user-end ciphertext to server-end ciphertext reduces the possibility of privacy leakage due to cooperation between two servers or participants and servers. To avoid a rise in the rounds of interaction caused by the increase in the number of participants, a multi-party random numbers aggregation method is proposed based on verifiable secret sharing. Ensuring the sensitive data remains undisclosed while enhancing the global model’s precision. Theoretical analysis and experimental evaluation have both demonstrated that this privacy-preserving deep learning scheme is equipped to handle multiple keys, resist collusion attacks, and achieve higher accuracy.

EFFICIENT AND EXPRESSIVE SEARCH OVER ENCRYPTED DATA WITH MULTI FACTOR AUTHENTICATION SCHEME

More data owners are tempted to outsource their data to cloud services as a result of the development of cloud storage. Sensitive data should be encrypted before being outsourced due to privacy concerns. To assure data availability, a variety of searchable encryption techniques are used. However, the effectiveness of data consumers' queries is not given much consideration by the current search algorithms, particularly in the case of several owners. In this study, linguistic coefficients are used to create an encrypted search index. In particular, the encrypted search index utilizing Lagrange co-efficient makes use of secure inner-product calculation for both search and relevance assessment by taking into account a sizable amount of data in the cloud. A system architecture of preferred search over encrypted data in the cloud scene is built, and the requirements in terms of efficiency and privacy are specified. This enables the clo ud servers to do a secure search without knowing any sensitive data (e.g., keywords and trapdoors).For each data owner, the Lagrange coefficient is used to build indices that can support searches over numerous keyword fields and enable accu rate relevance computation in order to produce an efficient search.To protect user data privacy, a multifactor authenticati on technique might be utilised.After logging in to the system, the user will receive three different sorts of authentications, including an OTP utilizing a number, a captcha, and an OTP image. This picture will serve as a key. In order to accomplish secure search and relevance score calculation, the user's preference and the search query are expressed in vector form. Additionally, Preferred Search over Encrypted Data (PSED) is consistent with cloud computing's scalability and adaptability. Finally, a thorough security study verifies the security of our scheme, and a performance analysis highlights its effectiveness and efficiency. Keywords: Encrypted Data, Inner product calculation, Multifactor authentication, Data privacy

Innovative Application of Blockchain Technology for Digital Recipe Copyright Protection

With the advent of the digital age, traditional lifestyle activities, such as reading books, referencing recipes, and enjoying music, have progressively transitioned from offline to online. However, numerous issues plague the conventional approach to digital copyright protection. This is especially true in the realm of recipe protection, where the rights and interests of original creators are inadequately safeguarded due to the widespread dissemination of a large number of recipes on the Internet. This primarily stems from the high costs of gathering evidence, incomplete coverage of evidence collection, and the inability to identify and halt infringement activities in a timely manner during the process of traditional digital copyright protection. Therefore, this study designs and implements a blockchain-based digital recipe copyright protection scheme to address the issues of insufficient legal evidence and cumbersome processes in traditional digital copyright protection. First, we enhance standard short text similarity calculation method SimHash, boosting the accuracy of text similarity detection. We then utilize the decentralization, immutability, time-stamping, traceability, and smart contract features of blockchain technology for data privacy protection. We employ the Interplanetary File System (IPFS) to store raw data, thereby ensuring user privacy and security. Lastly, we improve the proxy voting node selection in the existing delegated proof of stake (DPOS) consensus mechanism. According thorough evaluation and empirical analysis, the scheme effectively improves the accuracy of text similarity detection. Simultaneously, the enhanced DPOS mechanism effectively rewards nodes with excellent performance and penalizes nodes exhibiting malicious behavior. In this study, we successfully designed and implemented an innovative digital recipe copyright protection scheme. This scheme effectively enhances the accuracy of text similarity detection; ensures the privacy and security of user data; and, through an enhanced DPOS mechanism, rewards well-performing nodes while penalizing those exhibiting malicious behavior.

RDPCF: Range-based differentially private user data perturbation for collaborative filtering

Collaborative filtering recommends potentially interesting content to users based on historical data that it collects from users, which can lead to privacy breaches by untrusted servers. Differential privacy is a strict definition of privacy, and user privacy protection in collaborative filtering through differential privacy methods has drawn widespread attention from researchers. However, perturbing user data in collaborative filtering through existing differential privacy techniques will lead to the problem of poor recommendation accuracy. This is because differential privacy techniques generally perturb user data independently and will destroy the data similarity required by collaborative filtering. This paper proposes a differentially private user data perturbation method RDPCF, which perturbs the user data within a given content similarity range, and constrains the perturbation probability by differential privacy definition, thus ensuring both the accuracy of using the perturbation results for collaborative filtering and user privacy protection. Experimental results show that the RDPCF considerably outperforms the existing methods regarding privacy protection level and recommendation accuracy.

An efficient medical image encryption algorithm for telemedicine applications

Applications for healthcare execute computations that support various patient diagnostics and save private user data on cloud servers. User data is being held at ransom as a result of increasing cyberattacks on medical systems. Additionally, unreliable external parties who sell private data for profit can access mathematical calculations on data stored in the cloud. In this research, we suggest an efficient Homomorphic medical image encryption algorithm protecting medical plaintext data against unauthorised access via homomorphic encryption. To increase the security level Paillier's encryption scheme (PES) is utilized to select the optimal key. Homomorphic encryption is a method of authentication which helps one to perform observes of the encrypted information through decrypting it. Secret sharing splits up computations among a number of edge virtual nodes and hides all arithmetic operations, preventing unreliable cloud servers from figuring out what was done with the encrypted patient data. Cloud computing resources help virtual edge nodes perform computationally difficult mathematical operations and lower latency in device-edge node data transmission. During the experimental study, different factors including execution time, encryption time, and decryption time are used to evaluate the performance of the suggested approach. The proposed technique's execution time is 2% faster than the currently used HE method and 4% faster than the ECC algorithm.

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems.

Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client's data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.

Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access

Dynamic spectrum access (DSA) has been introduced as a promising technology that allows a secondary system to access the licensed spectrum of the primary system to improve spectrum utilization. In this paper, we introduce Fed-MADRL by incorporating federated learning (FL) and multi-agent deep reinforcement learning (MADRL) to design a collaborative DSA strategy. Our Fed-MADRL scheme employs FL to enable multiple users to collaboratively optimize the system goal without sharing their training data. By keeping all the training data at the user end, FL improves the communication efficiency and strengthens user data privacy. To further reduce the communication overheads, each user only shares quantized information. We provide the convergence analysis to characterize the trade-off between the communication efficiency and the system performance. In particular, we show that the introduced method converges at a rate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/4</sup> ), where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> is the number of FL iterations. To the best of our knowledge, Fed-MADRL is the first work that utilizes FL in DSA networks under quantized communication. Performance evaluation results show that the introduced Fed-MADRL method outperforms the independent learning method and achieves comparable performance with the centralized MADRL method, which requires much higher communication overheads.