Privacy Protection Research Articles

Homomorphic Encryption for Secure Ad Targeting: Balancing Privacy and Personalization in Digital Advertising

This article explores the application of homomorphic encryption (HE) in secure ad targeting, addressing the critical challenge of balancing personalized advertising with user privacy concerns in the digital advertising ecosystem. We examine the fundamentals of HE, its integration into ad targeting processes, and propose a privacy-preserving ad platform architecture. Through a comprehensive feasibility analysis and performance evaluation, we assess the technical challenges, computational overhead, and scalability issues associated with implementing HE in real-time ad serving. Our findings indicate that while HE offers strong privacy guarantees, it currently faces limitations in terms of latency and throughput compared to traditional ad targeting methods. We analyze the trade-offs between privacy protection and targeting effectiveness, highlighting the impact on ad relevance and personalization. The article also discusses future directions, including advancements in HE algorithms, integration with other privacy-enhancing technologies, and regulatory considerations. By synthesizing current research and experimental results, this work provides valuable insights into the potential of HE to revolutionize privacy-preserving ad targeting, paving the way for a more secure and privacy-conscious digital advertising future.

PCDP-CRLPPM: a classified regional location privacy-protection model based on personalized clustering with differential privacy in data management

Abstract Location data management plays a crucial role in facilitating data collection and supporting location-based services. However, the escalating volume of transportation big data has given rise to increased concerns regarding privacy and security issues in data management, potentially posing threats to the lives and property of users. At present, there are two possible attacks in data management, namely Reverse-clustering Inference Attack and Mobile-spatiotemporal Feature Inference Attack. Additionally, the dynamic allocation of privacy budgets emerges as an NP-hard problem. To protect data privacy and maintain utility in data management, a novel protection model for location privacy information in data management, Classified Regional Location Privacy-Protection Model based on Personalized Clustering with Differential Privacy (PCDP-CRLPPM), is proposed. Firstly, a twice-clustering algorithm combined with gridding is proposed, which divides continuous locations into different clusters based on the different privacy protection needs of different users. Subsequently, these clusters are categorized into different spatiotemporal feature regions. Then, a Sensitive-priority algorithm is proposed to allocate privacy budgets adaptively for each region. Finally, a Regional-fuzzy algorithm is presented to introduce Laplacian noise into the centroids of the regions, thereby safeguarding users’ location privacy. The experimental results demonstrate that, compared to other models, PCDP-CRLPPM exhibits superior resistance against two specific attack models and achieves high levels of data utility while preserving privacy effectively.

Digital Due Process : Evaluating the Ethical Implications of Cloud-Based eDiscovery Tools in Criminal Investigations

This article examines the growing role of cloud-based eDiscovery in law enforcement and its implications for investigative efficiency and individual privacy rights. As cloud technologies revolutionize legal data processing, enabling real-time analysis of vast data volumes, law enforcement agencies are experiencing unprecedented acceleration in investigations and improvements in legal compliance. However, this technological leap raises significant privacy concerns, particularly regarding access to personal digital footprints such as emails, social media posts, and linked content. The article explores the delicate balance between leveraging cloud-based eDiscovery for enhanced law enforcement capabilities and safeguarding civil liberties in the digital age. Through analysis of case studies, legal frameworks, and emerging ethical guidelines, we evaluate current challenges and propose solutions for responsible implementation of cloud eDiscovery in law enforcement. The article underscores the urgent need for a comprehensive approach that harmonizes technological advancements with robust privacy protections, ensuring that the pursuit of justice does not come at the cost of individual rights in an increasingly digital world.

FHG-PR: a hybridized fuzzy-AHP and game theory model for assessing privacy risk on social media platforms

Social media platforms have become integral to our daily lives, enabling global connectivity and interaction. Current research provides general solutions for mitigating privacy risks, but it does not focus on particular platforms. However, the voluntary sharing of personal information on these platforms presents considerable privacy risks, highlighting the need for effective risk assessment mechanisms. This study presents a hybrid approach that uses the fuzzy Analytical Hierarchy Process (AHP) and game theory to estimate privacy risks on Meta and X. We used a fuzzy AHP to rank determinant variables based on surveys of social media users and professionals. These weighted criteria were then used in a Multi-Criteria Decision-Making (MCDM) framework using cooperative game theory to discover ways to lower privacy hazards. The model accurately analyzed the privacy threats on Meta and X, recommending alternate techniques for preventing breaches in data privacy. The cooperative game theory method allows stakeholders to collaborate on creating privacy-preserving measures. The findings emphasize the model’s ability to address platform-specific privacy threats, as well as its flexibility in other social media settings. The model’s potential for real-world application is demonstrated by its ability to provide practical risk reduction measures, which improve privacy protection for social media users.

Neurorights, Neurotechnologies and Personal Data: Review of the Challenges of Mental Autonomy

Objective: to present the results of a systematic review of research on the impact of neurotechnology on legal concepts and regulatory frameworks, addressing ethical and social issues related to the protection of individual rights, privacy and mental autonomy.Methods: The systematic literature review was based on the methodology proposed by a renowned British scholar, a professor emerita of computer science at Keele University Barbara Kitchenham, chosen for its flexibility and effectiveness in obtaining results for publication. Thorough searches were carried out with the search terms “neurotechnology”, “personal data”, “mental privacy”, “neuro-rights”, “neurotechnological interventions”, and “neurotechnological discrimination” on both English and Spanish sites, using search engines like Google Scholar and Redib as well as databases including Scielo, Dialnet, Redalyc, Lilacs, Scopus, Medline, and Pubmed. The focus of this research is bibliometric data and its design is non-experimental with a cross-sectional and descriptive, using content analysis based on PRISMA model.Results: the study emphasizes the need to establish clear ethical principles to protect individual rights and promote responsible use of neurotechnologies; a number of problems of mental autonomy were identified, such as improper handling of information, lack of legal security guarantees, violation of rights and freedoms in the medical sphere. The author shows the need to adapt the existing regulatory legal framework to address the ethical and social problems arising from the new neurotechnologies. It is noted that a broad study of neurotechnology issues will contribute to the protection of human rights.Scientific novelty: an expanded understanding of the five neurorights within the Universal Declaration of Human Rights is proposed; neurorights are viewed as a new category of rights aimed at protecting mental integrity against the misuse of neurotechnologies. The author justifies the adoption of such technocratic principles as personal identity, free will, mental privacy, equal access and protection against bias.Practical significance: the obtained results are relevant for understanding modern legal concepts related to neurorights and for adapting the existing normative legal acts to solve ethical and social problems arising from the emergence of new technologies, protection of human neurorights and liability for their violation. The study of these issues is key for provision of further responsible development and use of neurotechnologies.

Adjusting Self‐Protective and Non‐Response Behaviours in Sensitive Prevalence Estimation by a Two‐Stage Multilevel Randomised Response Technique

SummaryWhen using the customary direct questioning approach to collect sensitive information in a survey, some respondents may have no problem disclosing their true status or opinion, while others may be reluctant to reveal them. The problem of response bias is likely to arise as some responses tend to be more socially desirable than truthful. Hence, the indirect questioning approach can be used to guarantee privacy protection and reduce the influence of social desirability bias. This paper introduces a new technique which combines, both the direct and indirect questioning approaches and allows Bayesian estimates of the prevalence of multisensitive attributes to be obtained by taking into account the estimated proportion of honest respondents under direct questioning. Our proposal stems from a real survey in Taiwan and is illustrated with two motivating examples concerning voting behaviours and sexual identity. The empirical analysis reinforces that the proposed two‐stage multilevel method is satisfactory in mitigating the effects of respondents' self‐protective behaviour, and produces results that are more reliable than those based on the traditional direct questioning approach and better than a previous version of the multilevel randomised response method that ignores the presence of cheating behaviours in the direct questioning stage.

How to maintain trust, respect sovereignty and protect privacy: a new generation of international agreements on cross-border data access

How to maintain trust, respect sovereignty and protect privacy: a new generation of international agreements on cross-border data access

Research on Social Live Broadcast pps in Thenew Media Era

With the rapid growth of digital media, online social live streaming, as a new means of information exchange, has penetrated into our daily lives and has been widely used in various industries such as enterprise marketing, online teaching, and folk culture inheritance. This article provides a preliminary introduction to the concept, attributes, and classification of online social live streaming, and explores the application strategies in product promotion, online learning, and cultural promotion. When examining the current development status of social live streaming, a series of problems have been discovered, including the proliferation of vulgar information, user privacy and security issues, and intellectual property infringement. Suggestions have been put forward to improve the regulatory system for social live streaming content, strengthen user privacy and security protection, and promote the sound legal system, in order to promote the healthy and orderly development of social live streaming in the new media era and provide reference and inspiration for the industry.

Verifiable record of AI output for privacy protection: public space watched by AI-connected cameras as a target example

Verifiable record of AI output for privacy protection: public space watched by AI-connected cameras as a target example

LFL-COBC: Lightweight Federated Learning on Blockchain-Based Device Contribution Allocation

In the distributed cyber-physical systems (CPSs) within the industrial domain, the volume of data produced by interconnected devices is escalating at an unprecedented pace, presenting novel opportunities to enhance service quality through data sharing. Nevertheless, data privacy protection emerges as a significant challenge for data providers in wireless networks. This paper puts forward a solution integrating blockchain and lightweight federated learning, designated as LFL-COBC, which aims to tackle the issues related to data privacy and device performance optimization. We initially analyze multiple dimensions influencing the performance of computing devices, such as mining capacity, data quality, computational efficiency and local device deviation, which are crucial for augmenting user engagement. Based on these dimensions, we deduce a set of cooperation strategies for selecting the optimal committee members and rewarding the contributions of node devices equitably, thereby stimulating cooperation between users and servers. To intelligently and automatically detect device anomalies and alleviate the operational burden, a convolutional neural network (CNN) model is employed. Additionally, to address the escalating cost of customer participation and the potential data explosion issue, a near-optimal model pruning algorithm is designed. This algorithm can make the model obtained from the training of node equipment lightweight, thereby reducing the load of federated learning and the blockchain, as well as enhancing the overall efficiency of the system. The efficacy of our approach is demonstrated through numerical experiments on the HDFS and BGL public data sets. Experimental results indicate that the LFL-COBC scheme can effectively safeguard data privacy and optimize device performance concurrently, providing an effective solution for device anomaly detection in CPSs.

Secure Dual Network for Reversible Facial Image Anonymization Through the Latent Space Manipulation

We develop a method to automatically and stably anonymize and de-anonymize face images with encoder-decoder networks and provide a robust and secure solution for identity protection. Our fundamental framework is an NN-based encoder-decoder pair with a dual inferencing mechanism. We denote it as the Secure Dual Network (SDN), which can simultaneously achieve multi-attribute face de-identification and re-identification without any pre-trained/auxiliary model. In more detail, the SDN can take responsibility for successfully anonymizing the face images while generating surrogate faces, satisfying the user-defined specific conditions. Meanwhile, SDN can also execute the de-anonymization procedure and visually indistinguishably reconstruct the original ones if re-identification is required. Designing and implementing the loss functions based on information theory (IT) is one of the essential parts of our work. With the aid of the well-known IT-related quantity, Mutual Information, we successfully explained the physical meaning of our trained models. Extensive experiments justify that with pre-defined multi-attribute identity features, SDN generates user-preferred and diverse appearance anonymized faces for successfully defending against attacks from hackers and, therefore, achieves the goal of privacy protection. Moreover, it can reconstruct the original image nearly perfectly if re-identification is necessary.

Application of AI-Driven Medical Image Recognition in Precision Medicine and Healthcare

Abstract. Artificial Intelligence (AI) has been playing an important role in medical image recognition, especially in precision medicine and health management, where AI can improve the accuracy and efficiency of diagnosis by utilizing advanced technologies such as deep learning. This paper explores in detail the application of AI techniques in the early diagnosis of cancer, image analysis of cardiovascular diseases, and assessment of neuropsychiatric disorders. We also identify challenges in technical standards, data security and privacy protection, and interdisciplinary collaboration, and emphasize the importance of establishing effective technical standards and evaluation systems, as well as fostering collaboration among experts in various fields.

Navigating the intersection of computer technology and IoT: Innovations, challenges, and strategies for privacy protection

Abstract. The integration of computer technology in the Internet of Things (IoT) has introduced new efficiencies, and conveniences in many sectors. However, this integration has also brought some serious challenges, and one of the most pressing ones is the need for more effective protection of privacy. This paper investigates some of the latest security developments in IoT industry, including encryption, blockchain, and artificial intelligence. Furthermore, it highlights the important challenges in IoT technology by providing an overview of data overload, regulatory and security issues in IoT devices. Besides, possible solutions to these problems are suggested in this paper. In addition, a detailed conclusion is made, which includes three future plans and some important recommendations to researchers, practitioners, and policymakers who are interested in achieving effective IoT security and privacy. The paper will have a positive impact on the research community, as it will highlight the growing need to invest in research and collaboration to improve the security of the IoT ecosystems.

AI and Ethics: Moral Considerations of Automated Rumor Detection Technology

Abstract: As digital society continues to develop, social media and information distribution have experienced significant changes, leading to challenges with accessing credible information and identifying misinformation. In response, the use of AI automation technology has been advocated as a solution to identifying fake news and improving information credibility. However, implementing this technology raises ethical concerns related to data privacy protection, transparency in information monitoring, and maintaining a balance between free speech and censorship. This paper examines ethics associated with using AI automation techniques for identifying misinformation, and proposing relevant laws and ethical guidelines to address these concerns. Through a literature review and case analysis, this study provides guidance for the ethical implementation of AI automation techniques for identifying misinformation. Our research aims to contribute to the development of sound ethical practices in the use of AI automation techniques in misinformation identification. Furthermore, this study highlights the need for an integrated approach in AI automation technology implementation and the significant role of transparency and accountability in mitigating ethical concerns. Additionally, this paper explores the potential benefits and pitfalls associated with using AI automation techniques to identify misinformation. While such technology can improve information credibility and protect the democratic process, it also poses risks to the quality of information and public trust in technology. In conclusion, this study underscores the importance of ethical considerations in implementing AI automation techniques for identifying misinformation and provides guidance for policymakers and practitioners working in the field of AI and information governance.

Marketing Strategies in the Digital Age: Opportunities and Challenges

Purpose:Transformation and Response to Enterprise Marketing Strategies in the Digital AgeWith the rapid development of information technology, the digital era has come in all its aspects, bringing a new landscape to enterprise marketing strategy. The digital age brings many opportunities. First, there is more accurate market positioning, and enterprises use technologies such as big data, Can in-depth analysis of a variety of consumer data, accurate grasp of their needs and preferences, so as to clearly identify the target market, like millet through the analysis of user data, accurate positioning of young, the pursuit of cost-effective consumer groups, to launch products that meet their needs. Secondly, there is a wider distribution channel. The popularization of the Internet and social media has allowed enterprises to quickly spread their marketing information to all parts of the world, and Bytedance's products have utilized the advantages of their own platforms to achieve widespread information distribution. Moreover, more efficient customer interaction, enterprises can communicate with customers in real time through the online platform, timely access to feedback, improve customer satisfaction, such as Huawei through the online community interaction with users to improve products and services. However, the challenges cannot be ignored. Data security and privacy protection have become critical issues, and once a data breach occurs, it can seriously damage the reputation of a company, as some high-profile companies have been questioned over data security. The difficulty of accurate marketing is that the market changes rapidly, consumer demand is diverse and volatile, and it is difficult for enterprises to accurately grasp it at all times. The complexity of multichannel integration also causes problems for enterprises, and the characteristics and user groups of different channels are different, making integration difficult. Domestic and foreign enterprises have many successful experiences to learn from. For example, Apple pays attention to the user experience and has built a unique brand image and marketing model through the convergence of online and offline channels. In response to the challenge, enterprises should strengthen data security management and establish strict protection mechanisms. Continuous improvement of data analysis capabilities to better adapt to market changes; At the same time, we should focus on coordination between channels to achieve complementarity of strengths. In short, enterprises need to take full advantage of opportunities in the digital age, actively cope with challenges, and formulate effective marketing strategies in order to gain a foothold and develop in the fierce market competition.

Multi‐Objective Federated Averaging Algorithm

ABSTRACTThe recent global trend is the convergence of information and communications technology (ICT). By applying ICT in various fields such as the humanities, new types of products and services are created, and new values that help people's lives can be created. AI can be selected as a representative technology in such convergence ICT. However, applying AI technology to actual production requires ensuring data security. Federated learning (FL) can achieve secure sharing of data, where all parties participate in model training locally and upload it to the server for aggregation. The data never leaves the parties involved, thus solving the problems of data privacy and data silos. However, FL faces issues such as high communication cost, imbalanced performance distribution among participants, and low privacy protection. To achieve a balance between model accuracy, communication cost, fairness, and privacy, this paper proposes a multi‐objective optimization‐based FL algorithm (M‐FedAvg). The multi‐objective optimization problem of maximising the accuracy of the global model, minimising the communication cost, minimising the variance of the accuracy, and minimising the privacy budget is solved by NSGA‐III. The experimental results show that the algorithm proposed can effectively reduce the communication cost of FL and achieve privacy protection for participants without affecting the accuracy of the global model.

Thumbnail-Preserving Encryption Based on Binary–Decimal Hybrid and a Generalized Coupled Logistic Map

Traditional image encryption methods typically make a trade-off between privacy protection and visual usability. To address this issue, a novel thumbnail-preserving encryption scheme is proposed based on a hybrid of decimal and binary pixel values in three pixel groups, utilizing the excellent dynamic characteristics of a generalized coupled logistic map. Theoretically, it is proved that the core algorithm of this scheme is sum-preserving. Experimentally, it is demonstrated that this scheme not only excels in efficiency and resistance to attacks, effectively safeguarding image privacy, but also retains high-quality thumbnails after encryption, achieving both objectives of image privacy protection and visual usability.

Media image transmission and privacy protection for internet of things security

Internet of Things (IoT) is becoming more popular with the increase in advancements of technology and is widely used in various sectors. The data are collected from the real environment and then transmitted over the networks. Due to their restricted resources, processing capacity, and memory, IoT gadgets are susceptible to certain security risks. Several 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.

Ethical and Regulatory Challenges of Generative Artificial Intelligence in Healthcare: A Chinese Perspective.

To provide practical insights that delve into the ethical issues and regulatory implications of generative artificial intelligence (GenAI) in healthcare. Ethical Challenges and Regulatory Impact in China is used as an example. Despite China's efforts to strike a delicate balance between protecting public welfare and promoting technological advancement, numerous unresolved issues persist in the practical integration of generative artificial intelligence into healthcare settings. Key issues such as data application, privacy protection, cost-effectiveness and regulatory remain areas of ambiguity that require clarification. Stringent ethical guidelines, data privacy protection measures and continuous supervision and evaluation of artificial intelligence decisions will help enhance the expected benefits of GenAI in healthcare. The potential use of GenAI in healthcare has garnered widespread attention, emerging as a significant global research topic. However, its application in this domain presents substantial ethical and regulatory challenges. Compared to other fields, GenAI's role in healthcare is more sensitive and complex, necessitating an urgent assessment of its ethical implications for future development and deployment. Challenges and ethical considerations are particularly pronounced in developing countries with limited healthcare resources.

A Sustainable urban renovation assessment model (SURAM) for enhancing elderly-friendly environments

The rapid increase in the aging population has raised significant concerns about the living conditions and well-being of elderly residents in old communities. This study addresses these concerns by proposing a Sustainable Urban Renovation Assessment Model (SURAM) specifically designed to enhance elderly-friendly environments in Chongqing City. The model encompasses multiple dimensions, including the comfort of public facilities, service safety and convenience, medical travel services, infrastructure security, life service convenience, neighbor relations, ambulance aid accessibility, commercial service facilities, privacy protection, elderly care facilities and service supply, and medical and health facilities. By employing factor analysis, the study reduces the dimensionality of the 49 indicator factors, allowing for a more focused and comprehensive evaluation of the effectiveness of aging-friendly renovation efforts. The main factors identified in the proposed model include community infrastructure security, elderly comfort of community public facilities, completeness and convenience of surrounding living services, and security and convenience of elderly care services. The results reveal that the age-appropriate comfort of public facilities plays a significant role in achieving successful aging-appropriate renovation outcomes. The findings demonstrate that by addressing specific needs such as safety, accessibility, and convenience, communities can significantly improve the quality of life for elderly residents. Moreover, the application of SURAM provides actionable insights for policymakers, urban planners, and community stakeholders, guiding them in implementing targeted initiatives for sustainable and inclusive urban development.