Privacy Protection Research Articles

Blockchain and differential privacy-based data processing system for data security and privacy in urban computing

Recently, big data related to human movement, air quality, and meteorology have been generated in urban computing through sensing technology and the computing infrastructure. However, security problems arise as data utilization increases. If the sensing data from internet of things devices are constantly exposed, the users’ private information can be determined, a critical security risk that could result in privacy breaches. This paper proposes a secure data processing system using the blockchain and differential privacy for data security and privacy protection in urban computing. When a service provider requests information, the system generates it from urban computing data using machine learning. We apply differential privacy to these data to protect privacy. However, if a query repeats, differential privacy may provide insufficient privacy protection. Therefore, we reduce the total privacy cost by reusing noise for the same data and privacy parameters using the blockchain. Machine learning accuracy may decrease when noisy data are used for training. Thus, we increase accuracy by storing and appropriately using the model parameters generated by the same data in the blockchain. We design, simulate, and analyze the results of an experimental environment for reusing noise for differential privacy and parameter utilization of machine learning using the blockchain. The proposed approach reduces privacy costs compared to the existing mechanism while protecting data privacy. We demonstrate that, through parameter utilization, the accuracy improves compared to conventional mechanisms.

A decentralized federated learning-based cancer survival prediction method with privacy protection

BackgroundSurvival prediction is one of the crucial goals in precision medicine, as accurate survival assessment can aid physicians in selecting appropriate treatment for individual patients. To achieve this aim, extensive data must be utilized to train the prediction model and prevent overfitting. However, the collection of patient data for disease prediction is challenging due to potential variations in data sources across institutions and concerns regarding privacy and ownership issues in data sharing. To facilitate the integration of cancer data from different institutions without violating privacy laws, we developed a federated learning-based data integration framework called AdFed, which can be used to evaluate patients’ survival while considering the privacy protection problem by utilizing the decentralized federated learning technology and regularization method. ResultsAdFed was tested on different cancer datasets that contain the patients’ information from different institutions. The experimental results show that AdFed using distributed data can achieve better performance in cancer survival prediction (AUC=0.605) than the compared federated-learning-based methods (average AUC=0.554). Additionally, to assess the biological interpretability of our method, in the case study we list 10 identified genes related to liver cancer selected by AdFed, among which 5 genes have been proved by literature review. ConclusionsThe results indicate that AdFed outperforms better than other federated-learning-based methods, and the interpretable algorithm can select biologically significant genes and pathways while ensuring the confidentiality and integrity of data.

When Parents Request Nondisclosure: Rights of Adolescents to Access Their Health Information and Implications of the 21st Century Cures Act Final Rule.

AbstractDespite broad ethical consensus supporting developmentally appropriate disclosure of health information to older children and adolescents, cases in which parents and caregivers request nondisclosure continue to pose moral dilemmas for clinicians. State laws vary considerably regarding adolescents' rights to autonomy, privacy, and confidentiality, with many states not specifically addressing adolescents' right to their own healthcare information. The requirements of the 21st Century Cures Act have raised important ethical concerns for pediatricians and adolescent healthcare professionals regarding the protection of adolescent privacy and confidentiality, given requirements that chart notes and results be made readily available to patients via electronic portals. Less addressed have been the implications of the act for adolescents' access to their health information, since many healthcare systems' electronic portals are available to patients beginning at age 12, sometimes requiring that the patients themselves authorize their parents' access to the same information. In this article, we present a challenging case of protracted disagreement about an adolescent's right to honest information regarding his devastating prognosis. We then review the legal framework governing adolescents' rights to their own healthcare information, the limitations of ethics consultation to resolve such disputes, and the potential for the Cures Act's impact on electronic medical record systems to provide one form of resolution. We conclude that although parents in cases like the one presented here have the legal right to consent to medical treatment on their children's behalf, they do not have a corresponding right to direct the withholding of medical information from the patient.

Privacy Challenges and Solutions in Big Data Analytics: A Comprehensive Review

Abstract: Large data collections with a more intricate and diversified structure are referred to as "big data." These traits are typically associated with more challenges when it comes to storing, analyzing, and applying additional procedures or getting results. The technique of analyzing vast quantities of intricate data to uncover patterns or connections that are concealed is known as "big data analytics." Nonetheless, there is a clear conflict between big data's extensive use and its security and privacy. This paper examines privacy needs in big data, distinguishes between privacy and security, and focuses on privacy and security issues in large data. This study addresses the applications of privacy in business by utilizing established techniques like HybrEx, k-anonymity, T-closeness, and L-diversity. Many privacy-preserving techniques have been created for protection of privacy at various phases of the big data life cycle (such as data generation, storage, and processing). This work aims to explain the issues facing current privacy preservation strategies in big data and to give a comprehensive evaluation of them. This study also introduces new approaches to privacy preservation in big data, such as quick anonymization of massive data streams, identity-based anonymization, differential privacy, privacy preserving big data publishing, and hiding a needle in a haystack. This study discusses big data privacy and security issues in healthcare. A comparative analysis of some contemporary large data privacy approaches is also conducted. A comparative analysis of the latest big data privacy approaches is also conducted.

A Fake Products Identification by Using Blockchain

Abstract: Counterfeiting of products is a significant challenge across various industries, leading to economic losses and risks to consumer safety. This paper proposes a novel approach to combat counterfeit products using blockchain technology. By leveraging the immutable and transparent nature of blockchain, a framework is devised to authenticate products throughout their lifecycle. The system utilizes unique identifiers assigned to each product during manufacturing, which are then recorded on a blockchain ledger. Through a decentralized network of nodes, stakeholders including manufacturers, distributors, retailers, and consumers can verify the authenticity of products in real-time. Smart contracts are employed to automate verification processes, ensuring integrity and efficiency. Additionally, the framework incorporates mechanisms for data privacy protection, ensuring sensitive information remains secure. Through empirical analysis and simulations, the effectiveness and scalability of the proposed solution are evaluated. Results demonstrate a significant reduction in counterfeit incidents and enhanced trust among stakeholders. This research contributes to the advancement of anti-counterfeiting measures, offering a robust solution to safeguard product authenticity in the digital age.

Ethical keylogger solution for monitoring user activities in cybersecurity networks

This paper presents the development of a software keylogger to monitor user activities within a cyber network, enhancing security and facilitating comprehensive cybersecurity analysis. Guided by the agile methodology, the work adopts a flexible and iterative approach to development, ensuring continuous collaboration, adaptation to evolving requirements, and swift response to feedback. Leveraging the versatility and efficiency of the Python programming language, this work transforms the traditional notion of a keylogger, which is a program that records keystrokes, into an ethical and legally compliant tool. By capturing and logging keystrokes, the system provides valuable insights into user behavior and potential security threats, enabling proactive measures to prevent cyber-attacks and data breaches. Prioritizing transparency and consent, the system design adheres to ethical standards and privacy regulations, ensuring the protection of sensitive information and user privacy. This technical solution offers cybersecurity professionals, system administrators, and organizations a valuable tool for network monitoring while showcasing responsible development practices and contributing to the advancement of ethical cybersecurity solutions.

China’s privacy protection strategy and its geopolitical implications

How has China’s privacy protection strategy been developed, with its broad scope and stringent requirements for data localization and cross-border data flows? What are the broader geopolitical implications of its divergence from Western models of data privacy? This paper argues that China's privacy protection strategy, characterized by its comprehensive regulatory framework and government access to data, is redefining the contours of global data governance and creating new geopolitical fault lines. Drawing on official documents, laws, regulations, and a case study, this paper highlights the evolution of the regulatory framework in response to emerging challenges posed by technological innovations and the wider geopolitical environment. This paper contributes to the broader discussion regarding the implications of China's privacy protection approach, highlighting potential normative clashes with countries that favor a more open digital economy. China's efforts in developing its own privacy protection strategy have also resulted in the formulation of global standards for data privacy.

Conceptualizing ephemerality in online marketing communication for consumers and firms

Purpose Consumers send billions of messages with high ephemerality each day, yet the effects of this type of communication are relatively unknown. Online ephemeral communication refers to sending and receiving information with a predetermined, finite lifespan in computer-mediated environments. The purpose of this paper is to conceptually understand online marketing communications with high ephemerality relative to messages with low ephemerality within a consumer’s goal system. Design/methodology/approach This paper is an attempt to conceptually understand how high ephemerality differs from low ephemerality, particularly as online ephemeral communication has emerged and is widely used by consumers and firms. Goal systems theory is applied to understand how ephemerality is a means for consumers to reach their communication goals. Findings Consumers are more likely to use messages with high ephemerality to impress with narrowly relevant content, regulate emotions, build social relationships, persuade others through peripheral cues and protect privacy, but messages with high ephemerality are less likely to help consumers manage their impression, acquire or share useful information or present strong arguments. It is also proposed that messages with high ephemerality can help marketers increase interest through frequent peripheral cues, including fun and friendly content, drive sales by creating a sense of urgency and increase loyalty, but are less likely to increase awareness, build interest through flattering or informative content or drive sales through transactional messages. Research limitations/implications This study primarily advances the goal systems literature by introducing ephemerality. The defining feature of ephemerality, lifespan, also has research implications for studies of word-of-mouth marketing. The propositions in this study are ready for empirical investigation as to when consumers and firms choose to send messages with low or high ephemerality. Practical implications Firms need to understand how consumers are using messages sent with low and high ephemerality in order for firms to best move consumers through the sales funnel. Originality/value To the best of the author’s knowledge, this is the first paper to differentiate messages with low and high ephemerality, identify the presence and effects of ephemerality in offline and online communication and explain how and why sending messages with low or high ephemerality can help consumers and firms reach their communication goals. There is only one other paper in marketing on ephemerality in online marketing communications and no other conceptual work.

STP-KDE: A spatiotemporal trajectory protection and publishing method based on kernel density estimation

Promoted by the Internet of Thing era, the widespread use of mobile sensing devices equipped with positioning functions has led to the generation of substantial trajectory data. Mining and analyzing trajectory data has high research value, but poses a risk of user privacy leakage, resulting in fewer publicly available trajectory datasets for research and analysis. Therefore, a trajectory data publishing method that ensures high data utility while protecting user privacy has become a hot topic. In this paper, we propose a spatiotemporal trajectory data protection and publishing method based on kernel density estimation (STP-KDE), which protects the trajectory data and trajectory count values while improving data utility. In the protection process of trajectory data, we designed a kernel density clustering framework that is combined with the differential privacy exponential mechanism. In the protection process of trajectory counts value, the adaptive Laplace noise perturbation mechanism is proposed to differentially protect these counts. Experimental results show that STP-KDE can provide more useful data and stronger privacy protection than existing studies.

A Survey on Network Security Traffic Analysis and Anomaly Detection Techniques

With the increasingly severe network security situation, advanced network traffic anomaly detection techniques are urgently needed. This paper provides a comprehensive survey of the research status and latest progress in the field of network anomaly detection. Firstly, we introduce the basic concepts, common methods, and challenges of network traffic analysis, which lays the foundation for anomaly detection. Then, we systematically summarize the mainstream techniques in the anomaly detection field, including statistical methods, machine learning methods, deep learning methods, and behavior analysis methods, analyzing their basic principles, representative works, advantages and disadvantages, and applicable scenarios. Next, we focus on discussing the hybrid methods in the anomaly detection field, elaborating on the motivations, common strategies, and representative works of hybrid methods, and pointing out that hybrid methods are an important development direction for anomaly detection. In addition, the paper also summarizes the application effects of several types of methods in practical network security tasks and makes a quantitative comparison in tabular form. Finally, we prospect the future development trends of network anomaly detection techniques, proposing goals such as intelligentization, automation, federalization, and interpretability, while analyzing the challenges faced by anomaly detection, including data heterogeneity, complexity of security threats, model robustness, privacy protection, and interpretability. We argue that network anomaly detection requires interdisciplinary integration, strengthening of security big data governance, and a shift from passive defense to active immunity. As the strategic position of cyberspace security becomes increasingly prominent, driven by disruptive technologies such as big data, artificial intelligence, and blockchain, network anomaly detection will surely usher in new development opportunities and challenges.

Media Image Transmission and Privacy Protection for Internet of Things Security

Due to their restricted resources, processing capacity, and memory, Internet of Things (IoT) gadgets are susceptible to certain security risks. Encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.

Effectiveness of Telemedicine Interventions on Mental Health Outcomes during Pandemics in Brazil

Purpose: The aim of the study was to analyze the effectiveness of telemedicine interventions on mental health outcomes during pandemics in Brazil. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Research in Brazil shows promising results for telemedicine's effectiveness in improving mental health outcomes during pandemics. Telemedicine interventions, like virtual therapy sessions, enhance access to care, especially in remote areas. Patients express satisfaction with remote mental health support, citing convenience. However, challenges such as the digital divide and privacy issues require attention for equitable and confidential service delivery. Unique Contribution to Theory, Practice and Policy: Social cognitive theory (SCT), technology acceptance model (TAM) & health belief model (HBM) may be used to anchor future studies on analyze effectiveness of telemedicine interventions on mental health outcomes during pandemics in Brazil. Telemedicine platforms should be designed to accommodate Brazil's diverse cultural and linguistic landscape. Policy makers should develop clear regulatory guidelines for telemedicine practice in Brazil, particularly concerning licensure, privacy protection, and reimbursement policies.

LSH Models in Federated Recommendation

Given the challenges in recommendation effectiveness, communication costs, and privacy issues associated with federated learning, the current algorithm amalgamates locality sensitive hash (LSH) with three federated recommendation models: Generalized Matrix Factorization, Multilayer Perceptions, and Neural Matrix Factorization. First, the participation weights of the model are determined based on the participation degree of the federated learning clients to improve the efficiency of joint learning. Second, the local parameters of the federated aggregation model are divided into two groups to protect user embedding. Finally, rapid mapping and similarity retrieval of the upload parameters are performed using LSH to protect user privacy and shorten training time. We conducted experiments to compare the performance differences between LSH-based and Laplace noise-based differential privacy methods in terms of recommendation effectiveness, communication costs, and privacy preservation. Experimental results demonstrate that LSH models achieved a favorable balance between recommendation effectiveness and privacy protection, with improved time performance.

Context Privacy Preservation for User Validation by Wireless Sensors in the Industrial Metaverse Access System

The Industrial Metaverse provides unparalleled prospects for increasing productivity and efficiency across multiple sectors. As wireless sensor networks play an important role in data collection and transmission within this ecosystem, preserving context privacy becomes critical to protecting sensitive information. This paper investigates the issue of context privacy preservation for user validation via AccesSensor in the Industrial Metaverse and presents a technological method to address it. We explore the need for context privacy, look at existing privacy preservation solutions, and propose novel user validation methods that are customized to the Industrial Metaverse’s access system. This method is evaluated on time-based efficiency, privacy method and bandwidth utilization. Our method performs better as compared to the DPSensor. Our research seeks to provide insights and recommendations for developing strong privacy protection methods in wireless sensor networks that operate within the Industrial Metaverse ecosystem.

Integrating blockchain and ZK-ROLLUP for efficient healthcare data privacy protection system via IPFS

With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput.

Transformer-Based User Charging Duration Prediction Using Privacy Protection and Data Aggregation

Transformer-Based User Charging Duration Prediction Using Privacy Protection and Data Aggregation

Design and analysis of a memristive Hopfield switching neural network and application to privacy protection

Design and analysis of a memristive Hopfield switching neural network and application to privacy protection

Cryptography-based location privacy protection in the Internet of Vehicles

AbstractThe evolution of the Internet of Things paradigm in recent years demonstrate a significant impact on the transportation sector, leading to the emergence of a new research field, known as the Internet of Vehicles (IoV). In the IoV, vehicles can exchange information with each other and with the roadside units making use of Vehicular Ad Hoc Networks (VANETs). As this technology reaches near-to-market maturity levels, several issues arise related to the protection of users’ privacy, while the interest of adversaries for such private user data in IoV environments gets stronger. This paper aims to present a review of the state-of-the-art techniques tackling the protection of location privacy in IoV environments, as well as experimental evaluation findings regarding the usage of various cryptographic algorithms for the protection of information exchange in these networks. In the conducted evaluations, the AES algorithm has been used as the main standard, which has been coupled with several other encryption/decryption algorithms, such as RSA, ECC and NTRU. The metrics used for the evaluation include measurements over the key generation process, the certificate generation, the encryption/decryption times, the signature generation/verification times, etc. Moreover, the size of messages in the negotiation, the pseudonym exchange and the new pseudonym enabling phases has been recorded, while the energy consumption in the exchange pseudonyms phase has also been measured. All previous experiments have been carried out mainly on NS-3 and SUMO open-source software aiming to have an estimation of how the aforementioned algorithms behave under constrained resources such as CPU usage and power.

Privacy and Personal Information Protection by Social Media Companies in an AI era

Privacy and Personal Information Protection by Social Media Companies in an AI era

Federated Learning on Distributed and Encrypted Data for Smart Manufacturing

Abstract Industry 4.0 drives exponential growth in the amount of operational data collected in factories. These data are commonly distributed and stored in different business units or cooperative companies. Such data-rich environments increase the likelihood of cyber attacks, privacy breaches, and security violations. Also, this poses significant challenges on developing machine learning models on sensitive data that are distributed among different business units. To fill this gap, this paper presents a novel privacy-preserving framework to enable federated learning on siloed and encrypted data for smart manufacturing. Specifically, we leverage fully homomorphic encryption (FHE) to allow for computation on ciphertexts and generate encrypted results which, when decrypted, match the results of mathematical operations performed on the plaintexts. Multi-layer encryption and privacy protection reduce the likelihood of data breaches while maintaining the prediction performance of machine learning models. Experimental results in real-world case studies show that the proposed framework yields superior performance to reduce the risk of cyber attacks and harness siloed data for smart manufacturing.