Information Privacy Research Articles

Consumer interaction and privacy: the impact of in-feed lead generation ads on self-disclosure intention

PurposeAmidst the growing emphasis on privacy protection, this study aims to investigate how online interaction introduced in Lead Generation Ads (LGAs) affects consumers' Self-Disclosure Intention (SDI), particularly in the context of the Chinese advertising market.Design/methodology/approachThis research employs two scenario-based online surveys to analyse in depth the impact of LGAs on consumers' SDI. The first study collects valid feedback from 220 consumers through an online questionnaire to assess the direct effect of perceived interactivity on SDI. The second study, using an experimental design with a sample of 265 participants, further explores the mediating roles of perceived control and perceived vulnerability in the relationship between perceived interactivity and SDI and examines the moderating effect of privacy invasion experience (PIE).FindingsThis study reveals the significant and positive influence of perceived interactivity on SDI, with perceived control acting as a mediator that enhances this effect. Conversely, perceived vulnerability weakens the positive impact of perceived interactivity on SDI. Additionally, we explore the moderating role of PIE and find that it significantly influences the relationship between perceived interactivity and SDI. These findings underscore the importance of considering consumer privacy sensitivity, particularly in the design of interactive marketing strategies and within highly interactive advertising environments.Research limitations/implicationsOur research uncovers consumer privacy attitudes and behaviours in the Chinese market, providing insights into its unique dynamics of privacy and information disclosure. However, the geographical and cultural specificity of our study may limit its generalizability. Future studies should expand into various cultural and market contexts, considering the impact of digital technologies on consumer interactions and information disclosure, thereby enhancing the depth and applicability of global marketing strategies.Practical implicationsAdvertising platforms should explore online interactive communication methods to enhance consumers' perceived interactions and alleviate privacy concerns. Also, platforms should be designed with system security in mind to prevent the leakage and misuse of consumer data, thus increasing consumers' SDI.Social implicationsThe study provides insights for marketers on designing more effective and privacy-sensitive online advertising strategies in the Chinese market. Understanding the factors influencing consumers’ willingness to share personal information can lead to more effective engagement in digital marketing campaigns.Originality/valueBy integrating interactivity theory with privacy computing theory, this research provides a new perspective on the role of online interaction in consumer privacy protection and information disclosure decisions. The findings not only enrich the theoretical frameworks of interactive marketing and privacy protection but also offer empirical support for marketing practitioners in regard to designing interactive advertising strategies, especially balancing consumer privacy protection with the enhancement of shopping intentions.

Exploring Users' Mental Models and Privacy Concerns During Interconnected Interactions

Users frequently use their smartphones in combination with other smart devices, for example, when streaming music to smart speakers or controlling smart appliances. During these interconnected interactions, user data gets handled and processed by several entities that employ different data protection practices or are subject to different regulations. Users need to understand these processes to inform themselves in the right places and make informed privacy decisions. We conducted an online survey (N=120) to investigate whether users have accurate mental models about interconnected interactions. We found that users consider scenarios more privacy-concerning when multiple devices are involved. Yet, we also found that most users do not fully comprehend the privacy-relevant processes in interconnected interactions. Our results show that current privacy information methods are insufficient and that users must be better educated to make informed privacy decisions. Finally, we advocate for restricting data processing to the app layer and better encryption to reduce users' data protection responsibilities.

LEGAL PERSONS AND THE RIGHT TO PRIVACY

Abstract This article examines what the state of the law regarding the tortious protection of the privacy of corporations tells us about the concept of a legal person. Given that non-human persons are capable of having an interest in at least their informational privacy, logic would seem to dictate that they should be recognised such a right protecting their personality. In reality, the law is most hesitant to concede the right to privacy to non-natural persons (the same being true of reputation). This suggests that, for the dominant strand of the law at least, despite the rhetoric, legal persons do not really have rights of personality; in other words, that they are not really persons.

Road network traffic flow prediction: A personalized federated learning method based on client reputation

Accurate traffic flow prediction can provide effective decision-making support for traffic management, alleviate traffic congestion, and improve road traffic efficiency. Traffic flow data contains personal privacy information, such as vehicle trajectories, driving speed, etc. However, most existing research focuses on using all local data to jointly construct prediction models, facing data security and privacy issues. In response to these challenges, this paper presents a Personalized Federated Learning method based on Client Reputation for traffic flow prediction. This method avoids direct sharing of original data by performing model training and information aggregation locally, thereby better protecting user privacy. We initially constructed the foundational model by integrating Graph Neural Networks (GCNs) and Gated Recurrent Units (GRUs) for federated learning. GCNs are capable of capturing the spatial relationships between nodes in a road network, thereby extracting the spatial features of traffic flow. On the other hand, GRUs has advantages at handling sequential data, effectively capturing the dynamic temporal characteristics of traffic flow. Consequently, by combining GCNs and GRUs, we are able to build a model that captures both the spatial and temporal features of traffic flow. Subsequently, we employed a federated learning framework to train the model. In the training process, we propose two evaluation algorithms to evaluate the reputation of all clients participating in federated learning. Then, different weights are customized for each client through a personalized algorithm based on the client's reputation to improve its personalization. Finally, we validated the method on the License Plate Recognition (LPR) datasets collected in Changsha city, China. The experimental results indicate that the method can achieve good prediction performance and stability while protecting the privacy of all participants.

Robust and efficient three-factor authentication solution for WSN-based industrial IoT deployment

The relentless advancements in Cyber-Physical Systems (CPS) and Wireless Sensor Networks (WSN) have paved the way for various practical applications across networking, public safety, smart transportation, and industrial sectors. The Industrial Internet of Things (IIoT) integrates these technologies into complex, interconnected environments where vast amounts of data are transmitted between devices and systems. However, the inherent openness of communication channels in IIoT systems introduces distinctive security and privacy vulnerabilities, where malevolent entities can effortlessly intercept, forge, or delete communication messages. These vulnerabilities are exacerbated by the critical nature of industrial applications, where breaches can lead to significant operational disruptions or safety hazards To address these challenges, several authentication protocols have been proposed. Nevertheless, many of these protocols remain susceptible to various security attacks. To ensure privacy and security in IIoT environments, we introduce a robust and efficient authentication protocol for a WSN-based IIoT environment. This protocol preserves the privacy of information transmitted among all entities and provides an effective solution for securing this sensitive information. Additionally, we present a detailed security analysis of the proposed protocol to formally and informally demonstrate its security strength. The performance analysis is carried out to compare the proposed protocol against existing related protocols, with results unequivocally demonstrating that our protocol offers enhanced privacy and security with reduced costs.

Anonymous group structure algorithm based on community structure.

A social network is a platform that users can share data through the internet. With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. To mitigate this problem, an anonymous group structure algorithm based on community structure is proposed in this article. At first, a privacy protection scheme model is designed, which can be adjusted dynamically according to the network size and user demand. Secondly, based on the community characteristics, the concept of fuzzy subordinate degree is introduced, then three kinds of community structure mining algorithms are designed: the fuzzy subordinate degree-based algorithm, the improved Kernighan-Lin algorithm, and the enhanced label propagation algorithm. At last, according to the level of privacy, different anonymous graph construction algorithms based on community structure are designed. Furthermore, the simulation experiments show that the three methods of community division can divide the network community effectively. They can be utilized at different privacy levels. In addition, the scheme can satisfy the privacy requirement with minor changes.

General vs. specific: How location information control drives performance in peer-to-peer accommodation

Unlike traditional hotels that explicitly specify their geographical locations, hosts on peer-to-peer accommodation platforms can partially conceal their properties' location information, showcasing only a general area in public searches for potential guests. However, the impact of this information control has been underexplored. Drawing upon the communication privacy management theory and leveraging a unique Airbnb panel dataset from multiple sources, this study investigates the effect of partial location information concealment on property sales performance. Findings show that partially concealing location information leads to an 18.4% increase in occupancy rates compared to disclosing specific location details. Additionally, the effect of location concealment is contingent upon co-owner responsibility boundaries (i.e., host identity verification, guest screening), as well as environmental density boundaries (i.e., population density, listing density). Furthermore, a scenario-based experiment confirms the main effect, and reveals that guests’ sense of control and perceived risks serve as the underlying mechanisms driving this direct effect.

Real-time privacy-preserved auditing for shared outsourced data

Health providers need to share patient information across healthcare networks efficiently and securely to improve medical and health services. Timely data synchronization among relevant parties is crucial for effectively containing and preventing the worsening of the condition. However, ensuring rapid information sharing while maintaining the security of sensitive patient data remains a pressing concern. In this paper, we introduce a cloud storage integrity auditing scheme that can protect auditors from procrastinating and preserve the privacy of sensitive information. Our proposed system requires healthcare institutions to encrypt sensitive patient data before uploading it to the cloud. It mandates the use of a data sanitizer for the secure processing of encrypted data blocks. Auditors must verify data integrity and promptly submit their audit results to the blockchain within a predefined time frame. Leveraging the time-sensitive nature of blockchain technology, healthcare institutions can monitor auditor compliance within the allotted validation timeframe. We conducted comprehensive security analysis and performance evaluations to demonstrate the feasibility and effectiveness of our solution in addressing the challenges of secure and timely cloud storage in healthcare settings.

Health professionals’ perceptions of electronic health records system: a mixed method study in Ghana

BackgroundElectronic Health Record systems (EHRs) offer significant benefits and have transformed healthcare in developed countries. However, their implementation and adoption in low- and middle-income countries (LMICs) remains low due to challenges and competing interests. Health professionals’ perception of EHRs can influence their adoption and continued use. The objectives of this study are to explore the perception of health professionals regarding implemented EHR systems in three hospitals in Ghana and identify factors influencing their perception and satisfaction.MethodsIn this study, we employed a concurrent mixed method design to collect data from study participants from May to June 2023. The quantitative part employed a descriptive-survey and the qualitative (in-depth interview) techniques was applied. After obtaining written informed consent from each respondent, a structured survey questionnaire was filled out by the health professionals from three hospitals. An a priori power calculation was used to determine the sample size for the quantitative component. Two hundred and sixty-three (263) health professionals completed the questionnaire from the three facilities. A purposive sampling technique was used to select fifteen [1] participants for the interviews. A semi-structured interview guide was used for the in-depth interviews. The interviews were audio recorded, transcribed, and coded into themes using QSR Nvivo 12 software before thematic content analysis.ResultsOur findings revealed that 213 (80.99%) health professionals perceived the EHRs as beneficial to patients and were generally satisfied. An overwhelming majority, 197 (74.90%) of the health professionals, were satisfied with its use and expressed interest in continuing to use the system. The majority of health professionals viewed the EHRs to have improved their work and workflow processes and provided the desired results. However, few other health professionals were dissatisfied with the system because they viewed the EHRs as frustrating due to unstable internet connectivity and power supply. Other concerns were related to the privacy and confidentiality of patient information. They believe access to patient information should be on a need-to-know basis, and patient information should not be accessible to all other clinicians except those involved directly in their care processes.ConclusionThe study revealed that health professionals have a positive perception of the implemented EHRs, are highly satisfied with them, and are interested in continuing to use them. However, health professionals’ concerns about the unstable power supply, poor internet connectivity, security, and confidentiality of patient’s information need attention, to mitigate their frustrations and boost their confidence in the system.

Enabling Lignocellulose-Sourced Feedstocks to Fabricate Optical Filters with Desired Blocking Wavelength.

Near-infrared (NIR) filters have broad applications in fields such as information safety and privacy protection and environmental monitoring. Traditional NIR filters primarily rely on complex optical designs and environmentally unfriendly substrates, while lignocellulose-sourced NIR filters do not achieve the desired blocking wavelength and therefore face challenges in various application conditions. In this study, we propose a thickness adjustment strategy to precisely control the blocking wavelength of the NIR optical filters. The obtained optical filters with tailored thickness exhibited selective blocking wavelength in visible region (400-650 nm) as well as a high NIR transmittance (over 90%) and ultralow haze at 1400 nm. Given their selective wavelength blocking and high NIR transmittance, these bioselectors demonstrate potential applications in NIR optical fields, such as data security and privacy protection, presenting a promising advancement in next-generation sustainable NIR optical materials fabricated from all-lignocellulose feedstocks.

Fear of nothing to hide? How do Chinese people feel about privacy when facing facial recognition cameras?

ABSTRACT Facial-recognition cameras are becoming increasingly important for governments around the world to help maintain public security and improve public services. Despite these benefits, previous studies have shown that facial-recognition cameras may also cause risks such as privacy violations. This study explores how citizens respond to government-sponsored facial-recognition cameras in terms of privacy concerns in China, which is a country with a well-established digital system. By using a national survey combined with facial-recognition camera data, this study reveals that the presence of government-sponsored facial-recognition cameras does not lead to increased information privacy concerns among Chinese citizens. Instead, citizens in China are generally very willing to accept facial-recognition cameras built by governments, and this acceptance increases when citizens’ trust in companies and government institutions is greater. Our study suggests that the application of facial-recognition cameras has probably been normalised because people are more likely to consider the social and economic advantages of such application over the downside, specifically that related to privacy intrusion.

A Combination of Attribute-Based Encryption and Blockchain Access Control Scheme in Smart Home Environment

In response to the problems of excessive decryption computation burden on users, inability to protect users' privacy information, and the inability of traditional cloud storage methods to meet users' faster file upload and download speed requirements and track malicious users, this paper proposes an attribute-based encryption and blockchain-based trusted data access control scheme for smart home environments. The scheme combines symmetric encryption algorithms and attribute-based encryption algorithms to achieve fine-grained access control of data. At the same time, it leverages edge computing technology to outsource most of the decryption and computation operations to edge computing nodes, reducing the user's decryption and computation burden. Furthermore, the introduction of blockchain technology enables the monitoring and auditing of users within the system, achieving full traceability of access control. Finally, the proposed scheme was analyzed and validated through simulation experiments, which showed that the scheme is safe and effective, protecting users' security and privacy, and enabling secure data sharing.

Privacy-preserving coordination of power and transportation networks using spatiotemporal GAT for predicting EV charging demands

The gradual replacement of conventional-fuel vehicles by electric vehicles (EVs) in recent years provides a growing incentive for the collaborative optimization of power distribution networks (PDNs) and urban transportation networks (UTNs). However, the implementation of a centralized optimization model for PDNs and UTNs is currently unrealistic due to the requirement for establishing information privacy barriers between the two independently operated networks. The present work proposes a predict-then-optimize (PTO) framework that combines Spatiotemporal Graph Attention Network (STGAT) with enhanced queueing theory to achieve accurate prediction of EV charging demands. Building upon this, it efficiently coordinates optimization between the PDN and UTN, while preserving the information privacy of both independently operated networks. Specifically, the proposed STGAT model predicts vehicle flow by simultaneously exploiting the dynamic temporal and spatial dependencies in the historical traffic data of the target UTN. It then converts the predicted flow into spatiotemporal EV charging demands using an enhanced queuing model that considers the service capacity constraints of charging stations (CSs) and the behavior of EV users. Subsequently, the predicted EV charging demands are integrated into a multi-period PDN scheduling model. The results of numerical computations based on an IEEE 33-bus PDN and real-world traffic flow datasets demonstrate that the scheduling results provided by the proposed approach differ by only 0.5% compared to results obtained when applying actual EV charging demands.

A lightweight and secure authentication protocol for visually impaired and handicapped people in the telehealth system

Sensors and wearable technology are used to accumulate health-related data from the visually impaired (VI) and other special persons. This health-related data is disseminated via an open network channel, which poses numerous threats and requires rigorous attention for its protection. An appropriate security framework must be implemented on both the sender and the recipient sides to ensure the privacy and security of sensitive medical information for VI and disabled persons. The telehealth system will then benefit the VI and disabled people by improving their quality of life, telemonitoring, saving time and money, providing online support, and increasing the degree of trust and reliance on the system and the treatment of doctors. These practical benefits are not just theoretical but potentially life-changing for the VI and disabled community. Strong and flawless mutual authentication is necessary for the telehealth system to secure and preserve the privacy of VI and disabled people. Therefore, this article proposes a privacy-preserving asymmetric-based, lightweight, and robust security framework for the telehealth system operationalized for VI and disabled people. The proposed security framework alleviates all threats to sensitive information and preserves the privacy and security of the whole telehealth system. The security of the proposed framework has been verified through a well-known Real-Or-Random (RoR) model and simulated through a ProVerif software verification toolkit, while the performance analysis has been tackled by measuring computation, storage, and communication costs. The comparative analysis result revealed that the proposed framework outperforms its competitors, further reinforcing the hope about its potential impact.

Analysis of Trends in the Use of Artificial Intelligence in Diagnosis and Treatment

AI in healthcare has improved, making diagnostics more accurate and increasing the effectiveness of treatments. The present study discusses the AI trends in diagnostic and therapeutic applications and focuses on the presented practical applications and their effects on patient care. The purpose of this particular review is to focus on the current developments in the implementation of AI in the field of health care, present main use cases and successes, as well as discuss about the issues and concerns in the topic at hand. Previous studies on AI in healthcare with specific consideration of diagnostic image analysis and interpretation, histology and molecular pathology, whole-genome sequencing, and therapeutic decision support are discussed. The selection criteria included papers with data gathered from real-life AI cases and quantitative findings. Study materials were obtained from e-journals, conference papers, and established online sources with descriptive analysis being done on the data collected. A summary of the findings revealed a number of highly impactful subcategories focused on the use of artificial intelligence diagnostic imaging, especially in radiology, pathology, and genomics. The AI applications used in the fields of operations and drug discovery revealed the ability to accurately predict clinical trial outcomes and to create effective treatments. First of all, AI can become a game changer in healthcare by enhancing diagnostics accuracy and treatment outcomes. The future research questions include further developing the methods that explain the AI models’ decisions, protecting the privacy of patient information, and reducing algorithmic bias for better fair healthcare for all. Therefore, better interactions between creators of AI and clinicians and regulatory authorities are pertinent to make sure that the full advantages of AI are realized in clinical practice to advance patient care.

Empowering precision medicine: Insights from a national survey on pharmacogenomics knowledge, attitudes, and perceptions among community pharmacists in the UAE

BackgroundCommunity pharmacists are essential to pharmacogenomics implementation because they can help trainers, clinical advisors, and other medical professionals understand the importance of pharmacogenomics and encourage them to use it in their practice. This study is to evaluate the community pharmacists' understanding, attitudes, and perceptions of pharmacogenomics in the United Arab Emirates (UAE). MethodsProfessionals employed at community pharmacies in Abu Dhabi, Dubai, and the Northern Emirates participated in a cross-sectional study design. From July 2023 to February 2024, five pharmacy students in their last year conducted the survey. The study team employed a structured questionnaire to collect data in addition to conducting in-person interviews. The study questionnaire comprised three distinct sections namely, demographic information, knowledge of pharmacogenomics concepts, and perceptions regarding pharmacogenomics. ResultsA total of 586 pharmacists enrolled in the study. The average knowledge score regarding pharmacogenomics was 75.1 % with a 95 % confidence interval (CI) of [72.4 %, 77.7 %]. The average attitude score toward pharmacogenomics was 67.5 % with a 95 % CI of [66.3 %, 68.7 %]. Better pharmacogenomics knowledge among several groups: independent pharmacies (OR 1.7; 95 % CI 1.2–2.4), Pharmacists in Charge (OR 1.4; 95 % CI 1.3–2.02), pharmacists with 11–15 years of experience (OR 2.1; 95 % CI 1.4–4.2), graduates from international universities (OR 4.6; 95 % CI 1.6–12.9), and those who received training on pharmacogenomics (OR 11.9; 95 % CI 3.3–14.5). Similarly, better attitude scores were observed among independent pharmacies (OR 1.5; 95 % CI 1.1–2.1), Pharmacists in Charge (OR 1.5; 95 % CI 1.07–2.1), pharmacists with 16–20 years of experience (OR 2.1; 95 % CI 1.16–3.7), graduates regional universities (OR 1.47; 95 % CI 1.05–2.1), and those who received training on pharmacogenomics (OR 4.8; 95 % CI 3.2–7.3). ConclusionThe positive attitudes toward pharmacogenomics that we found in our research indicate that community pharmacists in the United Arab Emirates are beginning to realize the potential advantages of pharmacogenomics in terms of improving patient care. Policies ensuring the privacy and confidentiality of genetic information are also necessary in considering concerns about the availability of genetic test results to insurance companies and potential employers.

Speak, search, and stay: determining customers' intentions to use voice-controlled artificial intelligence (AI) for finding suitable hotels and resorts

PurposeThe present study investigates the customers' behavioural intention to use voice-based artificial intelligence (AI) to find the appropriate hotels and resorts in an emerging nation. This study determines the influences of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to obtain the appropriate information and find the location of the hotels and resorts.Design/methodology/approachThis study used a purposive sampling method for collecting data from the respondents, who are customers of the hotels and resorts in Bangladesh. A self-administered survey questionnaire was used to obtain responses from 378 respondents. After collecting the data, the reliability and validity of the constructs and hypotheses were tested via partial least squares structural equation modelling (PLS-SEM).FindingsThe findings of the study indicate that information quality, system quality, privacy and novelty value have a positive and significant impact on attitude and behavioural intention to use voice-based AI assistant services in an emerging nation. However, system quality does not significantly influence behavioural intention to use voice-based AI assistant but it has an indirect significant influence on behavioural intention through the mediation effect of attitude.Practical implicationsThe study’s findings provide essential guidelines for practitioners to understand the impacts of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to find the appropriate hotels and resorts to meet customers' needs and expectations.Originality/valueThis study contributes to the existing literature on technology adoption by highlighting the interconnectedness of various factors influencing users' behavioural intentions. The study’s focus on an emerging nation provides a valuable theoretical contribution. It highlights that user perceptions and attitudes towards technology adoption may differ from those in developed nations due to unique contextual factors.

Examining consumers attitude towards E-wallet utilization as a payment method using technology acceptance model: A perspective from young generations in Nueva Ecija, Philippines

This study aims to provide valuable insights into how consumers interact with e-wallets in order to understand their behaviors and preferences. This perspective and behavior can help businesses and institutions better understand consumer behavior and develop strategies to increase e-wallet adoption. This research study is descriptive in nature. The researchers utilized frequency, percentage, weighted mean, and pearson R as statistical treatment. According to the findings, majority of respondents stated that Information Privacy is the most common and most encountered problem related to their use of e-wallets, while few reported the fewest, which were Unauthorized Application Updates. The statement implies that the majority of respondents were concerned about their information's security and safety. According to respondents, fear stems from concerns about others gaining unauthorized access, data breaches, or the misuse of sensitive information. Conduct interviews and surveys in your community to find out why people aren't using e-wallets. The researchers recommended implementing training programs on e-wallet usage to educate the community through focused campaigns, enhancing the usability and accessibility of the application for ongoing enhancement.

Strategic framework and global trends of national smart education policies

Integrating intelligent technology to promote smart education, had become a focus of education policies in many countries. However, there was little literature on strategies and trends of smart education policies at the national level. This study investigated the framework for developing national smart education strategies and trends of national policy making on smart education. Based on the Delphi technique method, a National Smart Education Framework was developed, consisting of four essential leveraging points: (1) forward-thinking governance and policy initiatives, (2) digital learning environments conducive to smart education, (3) transformative teaching and learning enabled through technology, and (4) overarching considerations. Additionally, a textual analysis method was employed to analyze 24 smart education policies from 24 countries or organizations, to uncover the strategies and policy trends of smart education in accordance with the proposed smart education framework. Promoting high-quality, inclusive, and accessible education, increasing Internet connectivity and access to digital tools, enhancing digital skills, ensuring information security and privacy, implementing new digital pedagogies, providing real-time feedback, emphasizing critical thinking, problem-solving, and creativity, etc. were the main trends for smart education policy making. The findings of this study provide valuable insights for policymakers and educators in shaping smart education policies and practices worldwide to promote sustainable development.

A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system

With the widespread use of mobile communication and smart devices in the medical field, mobile healthcare has gained significant attention due to its ability to overcome geographical limitations and provide more efficient and high-quality medical services. In mobile healthcare, various instruments and wearable device data are collected, encrypted, and uploaded to the cloud, accessible to medical professionals, researchers, and insurance companies, among others. However, ensuring the security and privacy of healthcare data in the context of mobile networks has been a highly challenging issue. Certificateless signature schemes allow patients to conceal their respective privacy information for different sharing needs. Nevertheless, existing mobile healthcare data protection solutions suffer from costly certificate management and the inability to restrict signature verifiers. This paper proposes a certificateless designated verifier sanitizable signature for mobile healthcare scenarios, aiming to enhance the security and privacy of mobile healthcare data. This scheme enables the sanitization of sensitive data without the need for certificate management and allows for the specification of signature verifiers. This ensures the confidentiality of medical data, protects patient privacy, and prevents unauthorized access to healthcare data. Through security analysis and experimental comparisons, it is demonstrated that the proposed scheme is both efficient and effectively ensures data security and user privacy. Therefore, it is well-suited for privacy protection in mobile healthcare data.