Privacy Policies Research Articles

I Don’t Want to Sound Rude, but it’s None of their Business: Exploring Security and Privacy Concerns Around Assistive Technology Use in Educational Settings

Students with disabilities often use assistive technology to gain equal access to educational content. However, using this technology can lead to security and privacy concerns, including the possibility of disclosing a disability. In this work, we explore that and other privacy risks happening through the use of assistive technology in educational settings. We conducted interviews with eight students who use assistive technology, exploring the risks around in-person and online classes and how they changed as students moved to remote courses during the COVID-19 pandemic. We found that students were aware of in-person risks, but generally had not thought of the potential risks of online disclosure. Students were concerned about their disability being disclosed, especially without their explicit consent, but they considered trade-offs, including increased accessibility for themselves and others, that might make them more accepting of disclosure. Building on the interviews, we did an analysis of the privacy policies of the assistive technology that the students used, examining how potential privacy and security vulnerabilities were communicated. We found that, although there are identified vulnerabilities for many of the assistive technologies, the policies varied widely in whether or how much information they gave the user about the risks.

Understanding the Factors Affecting Trust and Security in Online Transactions for Maid Service Websites

In today's digital landscape, trust and security hold significant importance, especially in the realm of online transactions, particularly on maid service websites. This paper delves into the complex factors that influence users' perceptions of trust and security when engaging with these platforms. Through a thorough examination of existing literature and empirical analysis, this research identifies the key factors that shape users' perceptions of trust and security during online transactions within the maid service industry. The study explores various aspects, including website design, encryption protocols, payment gateways, privacy policies, and customer reviews, among others, to understand their impact on users' trust and security perceptions. Additionally, it investigates how factors like customer demographics, previous online experiences, and awareness of cybersecurity threats contribute to shaping these perceptions. Furthermore, the research delves into the strategies and best practices adopted by maid service websites to enhance trust and security. This includes implementing secure payment gateways, transparent communication channels, and robust data protection measures. The insights gained from this study provide a deeper understanding of the dynamics influencing consumer trust and security in the context of maid service websites. These findings offer valuable implications for industry practitioners to improve trust-building strategies and strengthen security measures, ultimately creating a safer and more trustworthy online environment for users. Keywords: MaidIn, Maid Service, Maid Jobs, Maid Service near me, Maid Service Project, Maid Service full Stack Web Development

Assessing the Level of Cybersecurity Awareness and Practices of Application Users and Their Impact on Privacy Policy Consents: A Case Study in Application Downloading

The aim of this study is to understand the level of cybersecurity awareness and practices of application users and determine the impact on their consent to privacy policies through a case study in application downloading. To achieve this aim, this study employs quantitative research methodology. The primary data collection instrument is a structured questionnaire designed to gather insights and perspectives from 65 participants of application users in the Kingdom of Saudi Arabia, including several types of applications. The collected data was collected and analyzed using SPSS software. The findings revealed that most of the study participants disagree that they understand the importance of cybersecurity measures when using mobile applications with a frequency of 27 and a percentage of 41.5%. Also, most of the study participants disagree that they are aware about negative cybersecurity practices with a frequency of 26 and a percentage of 40%. Furthermore, only 23 participants are paying attention to Download applications from only official app stores or trusted sources with a percentage of 35.4% The findings of the study also revealed that a p-value related to Pearson Chi-Square is (0.019) which is less than a significance level of (0.05), this proves a significant statistical relationship between cybersecurity awareness and practices of application users on privacy policy consents.

Establishing Consumer Trust Through Data Protection Law as a Competitive Advantage in Indonesia and India

Data protection laws play a crucial role in enhancing consumer trust in the digital economy, especially with the rise of online cybersecurity threats due to firm expansion. Despite advances, personal data protection laws remain controversial globally, with challenges in implementation hindering progress in some regions. This research examines the competitive advantage of building consumer trust through the Personal Data Protection Law. This research uses juridical-normative research with a legislative approach and comparison with other countries. A comparison was made with India, one of the countries with higher awareness of the importance of personal data protection laws than other countries. The results show that Consumer trust in a company is measured by the extent to which they believe the company will protect their data. Public privacy policies and government regulations that enforce data security measures and increase transparency positively impact trust by reducing concerns about privacy risks. Although data protection laws have been introduced, obstacles such as lack of legal assistance and overlapping sectoral regulations still exist, hindering the smooth implementation of these laws. Nevertheless, implementing data protection laws, such as Indonesia's Private Data Protection Law No. 27 of 2022 and India's Digital Personal Data Protection Act of 2023, is crucial for boosting consumer confidence and regulating data processing. Data security is critical in establishing and maintaining consumer trust in companies as online cybersecurity threats increase with business expansion.

Reconciling the conflicting goals of privacy protection and competition policy through making platform use data saleable – an institutional perspective on data markets

PurposeIt has become increasingly clear that the objectives of privacy and competition policy are in conflict with one another with regard to platform data. While privacy policies aim at limiting the use of platform data for purposes other than those for which the data were collected in order to protect the privacy of platform users, competition policy aims at making such data widely available in order to curb the power of platforms.Design/methodology/approachWe draw on Commons' Institutional Economics to contrast the current control-based approaches to ensuring the protection as well as the sharing of platform data with an ownership approach. We also propose the novel category of platform use data and contrast this with the dichotomy of personal/non-personal data which underlies current regulatory initiatives.FindingsWe find that current control- and ownership-based approaches are ineffective with regard to their capacity to balance these conflicting objectives and propose an alternative approach which makes platform data saleable. We discuss this approach in view of its capacity to balance the conflicting objectives of privacy and competition policy and its effectiveness in supporting each separately.Originality/valueOur approach clarifies the fundamental difference between data markets and other concepts such as data exchanges.

Read or skip privacy policies when installing apps on wearable devices: the roles of perceived necessity and threat clues

Wearable devices are increasingly integral to our daily lives but raise significant concerns about the security of personal data. In contrast, users often skip reading privacy policies when installing apps on wearable devices. This gap between the concerns and behaviors has spurred disputes regarding business ethics between app providers and users when private information is leaked. While much research has explored the reasons underlying users’ skipping behaviors on mobile phones or tablets, there has been limited attention given to wearable devices despite their prevalence. Unlike mobile phones or tablets, wearable devices often have limited capabilities, necessitating users to install essential apps and perceive a strong sense of necessity. This research, using both eye tracking and self-report methods, investigates how this perceived necessity affects users’ attention to privacy policies on wearable devices during app installations and how it interacts with the presence of threat clues – a key factor that influences behaviors toward privacy policies. In Study 1, eye tracking technology is applied to observe the variation in users’ attention toward privacy policies. Results indicate that when users perceive a high (vs. low) necessity for an app, they pay less attention to its privacy policy. This effect is amplified when there are strong threat clues present. In Study 2, based on the process-dissociation procedure (PDP), the cognitive mechanisms underlying user reading behavior are uncovered. The findings show that users with high perceived necessity are less inclined to engage in analytical processing when reading privacy policies, and this tendency is further strengthened by strong threat clues. This research supplements the existing literature on users’ behaviors toward privacy policies on wearable devices, and it also provides practical insights for prompting users to pay more attention to privacy policies and enjoy a secure digital experience.

Multi-step Transfer Learning in Natural Language Processing for the Health Domain

The restricted access to data in healthcare facilities due to patient privacy and confidentiality policies has led to the application of general natural language processing (NLP) techniques advancing relatively slowly in the health domain. Additionally, because clinical data is unique to various institutions and laboratories, there are not enough standards and conventions for data annotation. In places without robust death registration systems, the cause of death (COD) is determined through a verbal autopsy (VA) report. A non-clinician field agent completes a VA report using a set of standardized questions as guide to identify the symptoms of a COD. The narrative text of the VA report is used as a case study to examine the difficulties of applying NLP techniques to the healthcare domain. This paper presents a framework that leverages knowledge across multiple domains via two domain adaptation techniques: feature extraction and fine-tuning. These techniques aim to improve VA text representations for COD classification tasks in the health domain. The framework is motivated by multi-step learning, where a final learning task is realized via a sequence of intermediate learning tasks. The framework builds upon the strengths of the Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo) models pretrained on the general English and biomedical domains. These models are employed to extract features from the VA narratives. Our results demonstrate improved performance when initializing the learning of BERT embeddings with ELMo embeddings. The benefit of incorporating character-level information for learning word embeddings in the English domain, coupled with word-level information for learning word embeddings in the biomedical domain, is also evident.

Filling the Void: How E.U. Privacy Law Spills Over to the U.S.

There is mounting evidence that the EU’s General Data Protection Regulation (GDPR) has influenced the information privacy policies and practices that firms adopt in relation to people outside of the E.U., even when that is not required by the E.U. regulation. We use a hand-coded dataset of privacy policies from firms’ U.S. and E.U.-facing websites to document and explain these kinds of international regulatory spillovers. Our findings are consistent with the hypothesis that spillovers are driven by the costs of complying with different standards in different parts of the same firm. In fact, 75% of the firms in our sample use the same privacy policy for their U.S. and E.U.-facing websites. At the same time, our findings do not support the conclusion that firms comply with the GDPR in their U.S.-facing privacy policies out of fear of being sanctioned if the policy is somehow applied to E.U. residents. Finally, we find that spillovers are more prevalent among firms with a physical presence in the E.U. This suggests that international networks of compliance professionals may play a significant and understudied role in regulatory compliance, perhaps by providing channels for norms and resources to move across borders.

RRefined and Effective Integration of Access Control Mechanism in Blockchain Enabled DApp

The combination of the Internet of Things (IoT) and aviation integration with the help of satellite and 6G communication technologies has led to the Internet of Unmanned Aerial Vehicles (UAV) or Internet of Drones (IoD). To host and share large amounts of drone data in real time, cloud-based IoD algorithm Cloudbased IoD is the wrong choice to reduce the weight of mobile drones. However, how to protect the sensitive drone at the fair is a matter of curiosity, and it is very difficult to break the environment where drone use is available and limited resources are available. Although our previous work in SPNCE21 (PATLDAC) developed an air-on middle of the main load of based drone data access control system with privacy policy, limited access time and user tracking, it provides conflicting and centralized cloud data storage for cloud environment and access with unreliable data. It is not reliable in terms of data access and tracking of users. To this end, we propose a blockchainbased privacy data access control (BPADAC) scheme for distributed and secure drone data sharing on cloudbased IoDs. Building on the granular, traceable, and privacypreserving drone data access capabilities of our previous work, we extend this by using blockchain and decentralized hash tables (DHT) to provide secure and reliable wireless access to humanmachine data and storage. as well as reliable and limited access guarantee mechanisms for cloud drone data sharing services. We also set up a public and denial user tracking system to prevent user abuse and deny the traitor. Finally, we propose a prototype for security analysis and performance evaluation using smart contracts on the Ethereum blockchain to demonstrate the feasibility of BPADAC. hash table, attributebased ciphertext encryption policy, and blockchain-based privacy-protected enabled and aware data access control Key Words: Blockchain, UAV,IoD, Distributed Hash Table, Ciphertext-Policy-Attribute-Based-Encryption, blockchainbased privacy-aware data access control

Data Security and Consumer Trust in Fintech Innovations using Technology Adoption Method

This study investigates the interplay between data security and consumer trust in the context of fintech innovation, employing the Technology Adoption Model (TAM) as the theoretical framework. Fintech, characterized by its rapid technological advancements, has revolutionized traditional financial services, offering convenience and efficiency. However, concerns about data security and privacy have emerged as significant barriers to consumer adoption and trust. The research methodology involves a comprehensive literature review to establish the theoretical foundation and empirical analysis of consumer perceptions through surveys and interviews. The TAM framework, which examines users' attitudes and behaviors towards technology adoption, serves as a lens to understand how data security measures influence consumer trust in fintech platforms. Results indicate that consumers prioritize data security and privacy when evaluating fintech services, with perceptions of security significantly impacting trust levels. Factors such as encryption protocols, authentication mechanisms, and regulatory compliance play crucial roles in shaping consumer perceptions of data security. Additionally, transparency and communication regarding data handling practices enhance trust and mitigate concerns. The study identifies the role of user experience design in fostering trust, as intuitive interfaces and clear privacy policies contribute to perceived security. The findings underscore the importance of proactive measures by fintech companies to address data security concerns, including robust cybersecurity protocols, regulatory compliance, and transparent communication strategies. The study highlights the intricate relationship between data security and consumer trust in fintech innovation, emphasizing the need for comprehensive approaches to mitigate risks and build consumer confidence. By understanding and addressing consumer perceptions and concerns, fintech companies can enhance trust and drive widespread adoption of innovative financial technologies. Keywords: fintech, adoption, security, trust, inclusion, investment, payment, insurance, technology, efficiency, innovation.

PrivData Network: A Privacy-Preserving On-Chain Data Factory and Trading Market

Privacy concerns often raise when sensitive data are collected, traded, and processed. The data owner typically loses her ultimate control of the data after data-outsourcing. In this work, we present the PrivData Network – a community-controlled privacy-preserving data factory and trading market. It can be viewed as a standalone data ecosystem that enables privacy-preserving data-driven workflows in a controlled environment for orchestrating and automating data movement and data transformation. In particular, we design a data encapsulation mechanism with privacy assurance, which can guarantee data privacy, usage policy compliance and metadata validity. We also design a privacy policy language and utilize a static analysis library that transfers the program to the defined policy language. To ensure the correctness of data processing, we propose a publicly verifiable secure multiparty computation protocol for mixed circuits, which guarantees the output correctness even if all parties are corrupted. Its online efficiency is comparable to conventional semi-honest secret-sharing-based MPC schemes. Finally, we implemented a prototype of our system in C++ and benchmark it on various tasks, such as biometric matching, logistic regression, and decision trees, etc.

Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) enables fine-grained access control on ciphertexts, making it a promising approach for managing data stored in the cloud-enabled Internet of Things. But existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute content. Additionally, resource-constrained IoT devices, especially those adopting wireless communication, frequently encounter affordability issues regarding decryption costs. In this paper, we propose an efficient and fine-grained access control scheme with fully hidden policies (named FHAC). FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them. A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access policy. Dictionary attacks are thwarted by providing unauthorized users with invalid values. The heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud servers. Additionally, users can verify the correctness of multiple outsourced decryption results simultaneously. Security analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

Taypsi: Static Enforcement of Privacy Policies for Policy-Agnostic Oblivious Computation

Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data with other parties, typically by employing powerful cryptographic protocols to protect individual's data. One challenge when writing such functions is that most MPC languages force users to intermix programmatic and privacy concerns in a single application, making it difficult to change or audit a program's underlying privacy policy. Prior policy-agnostic MPC languages relied on dynamic enforcement to decouple privacy requirements from program logic. Unfortunately, the resulting overhead makes it difficult to scale MPC applications that manipulate structured data. This work proposes to eliminate this overhead by instead transforming programs into semantically equivalent versions that statically enforce user-provided privacy policies. We have implemented this approach in a new MPC language, called Taypsi; our experimental evaluation demonstrates that the resulting system features considerable performance improvements on a variety of MPC applications involving structured data and complex privacy policies.

Strategic Indeterminacy and Online Privacy Policies: (Un)informed Consent and the General Data Protection Regulation

AbstractArticle 12 of the General Data Protection Regulation (GDPR) requires data controllers to provide data subjects with any information relating to data processing operations “in a concise, transparent, intelligible and easily accessible form, using clear and plain language.” Linguistic inclusivity of privacy policies is no longer a matter of style, but has been a binding legal requirement under the new data protection framework. Article 5 GDPR sets forth the requirements of lawfulness, fairness and transparency and prohibits any data processing operations which do not meet the standards of specification, explicitness and legitimacy of processing purposes [29]. In this study, a quantitative and qualitative analysis of linguistic indeterminacy in a corpus of 350 online privacy policies is presented and it is argued that a considerable number of data controllers continue to make use of strategic vagueness in the context of purpose limitation, therefore potentially prejudicing compliance with the GDPR. A legal-linguistic perspective on the current challenges of informed consent in European data protection law is provided. Finally, it is concluded that while the GDPR has contributed significantly to the linguistic empowerment of the data subject, the framework fails to satisfy the expectation of creating a participatory culture (see [36]) with a high degree of informational self-determination.

Peningkatan Trust Berbelanja Online pada Mahasiswa Perguruan Tinggi di Wilayah Bandung Berbasis Information Privacy Concern Dan Privacy Policy

Before entering the era of online shopping, previously information systems were only intended for internal company parties such as company employees. In this era of artificial intelligence, accounting information systems are also operated by consumers. Consumers operate information systems to be able to complete purchases of goods or services online. Based on research results through mass media, it is known that consumers' current shopping tendencies are more towards shopping online. The reason consumers tend to prefer shopping online is because the prices offered by online companies are cheaper and they save time shopping. When consumers make online shopping transactions, concerns arise about sharing personal data with e-commerce companies. These concerns include the use of consumers' personal data by e-commerce companies. This research is descriptive verification research using a survey approach. This research is classified as quantitative research. The respondents who will be studied in this research are students spread across universities in the city of Bandung. The data analysis method uses SEM-PLS. The results of the research show that information privacy concerns have a significant negative influence on trust, privacy policy has a significant positive influence on trust and simultaneously information privacy concerns and privacy policy have a significant positive influence on trust. So it can be concluded that if there is an increase in information privacy concerns and privacy policies, it will have an impact on increasing trust in online shopping for university students in the Bandung City area.

Validation and extension of two domain-specific information privacy competency models

The purpose of this paper is to validate two domain-specific information privacy competency models (IPCMs); the first for online consumers and the second for users of mobile applications (apps). For the validation of the competency models, we conducted qualitative research, using interviews to collect feedback by a group of nine information privacy experts. Regarding the evaluation, the experts commented largely positively for the structure and content of the IPCMs, as well as for the extent to which they achieve the intended goals. They also provided several points for improvements, which resulted in enhancing the quality of both IPCMs. The validation of the domain-specific demonstrated that this is the first study to empirically examine the privacy competencies that users of specific technological contexts should hold. The IPCMs can be used not only by educators and privacy policy makers for the design of privacy interventions, but also by e-commerce and mobile-apps providers, who could gain important insights into the way that they can be more reliable for their users. Both consumers and users of mobile-apps could benefit from IPCMs by acquiring the necessary privacy competencies through training programs for the protection of their information privacy.

Understanding Chinese Internet Users' Perceptions of, and Online Platforms' Compliance with, the Personal Information Protection Law (PIPL)

The Personal Information Protection Law (PIPL) was implemented in November 2021 to safeguard the personal information rights and interests of Internet users in China. However, the impact and existing shortcomings of the PIPL remain unclear, carrying significant implications for policymakers. This study examined privacy policies on 13 online platforms before and after the PIPL. Concurrently, it conducted semi-structured interviews with 30 Chinese Internet users to assess their perceptions of the PIPL. Users were also given tasks to identify non-compliance within the platforms, assessing their ability to address related privacy concerns effectively. The research revealed various instances of non-compliance in post-PIPL privacy policies, especially concerning inadequate risk assessments for sensitive data. Although users identified some non-compliant activities like app eavesdropping, issues related to individual consent proved challenging. Surprisingly, over half of the interviewees believed that the government could access their personal data without explicit consent. Our findings and implications can be valuable for lawmakers, online platforms, users, and future researchers seeking to enhance personal privacy practices both in China and globally.

Collective Privacy Sensemaking on Social Media about Period and Fertility Tracking post Roe v. Wade

On June 24, 2022, the U.S. Supreme Court overturned Roe v. Wade, which has led to full bans on most abortions in 14 states within one year. Many people in the U.S. use period and fertility tracking apps for reproductive healthcare and concerns have arisen about the privacy risks these apps might pose in the wake of Roe reversal. Existing literature on privacy risks of period and fertility tracking apps has primarily examined the privacy policies and practices of these apps. However, how users make sense of the privacy risks of these apps, especially in the post-Roe time, remains understudied. This study explores collective privacy sensemaking on social media, a practice in which people collectively make sense of a privacy situation. Our findings reveal how people contextualize privacy issues, speculate about the associated risks, as well as explore risk mitigation strategies. We conclude with privacy design implications for privacy design in period and fertility tracking apps and contribute insights that could inform policymaking and legal perspectives.

Comparing preferences for skin cancer screening: AI-enabled app vs dermatologist

Background and aimSkin cancer is a major public health issue. While self-examinations and professional screenings are recommended, they are rarely performed. Mobile health (mHealth) apps utilising artificial intelligence (AI) for skin cancer screening offer a potential solution to aid self-examinations; however, their uptake is low. Therefore, the aim of this research was to examine provider and user characteristics influencing people's decisions to seek skin cancer screening performed by a mHealth app or a dermatologist. MethodsTwo forced-choice conjoint experiments with Nmain = 1591 and Nreplication = 308 participants from the United States were conducted online to investigate preferences for screening providers. In addition to the provider type (mHealth app vs dermatologist), the following provider attributes were manipulated: costs, expertise, privacy policy, and result details. Subsequently, a questionnaire assessed various user characteristics, including demographics, attitudes toward AI technology and medical mistrust. ResultsOutcomes were consistent across the two studies. The provider type was the most influential factor, with the dermatologist being selected more often than the mHealth app. Cost, expertise, and privacy policy also significantly impacted decisions. Demographic subgroup analyses showed rather consistent preference trends across various age, gender, and ethnicity groups. Individuals with greater medical mistrust were more inclined to choose the mHealth app. Trust, accuracy, and quality ratings were higher for the dermatologist, whether selected or not. ConclusionOur results offer valuable insights for technology developers, healthcare providers, and policymakers, contributing to unlocking the potential of skin cancer screening apps in bridging healthcare gaps in underserved communities.

Fair Data is the New Black: Online Shopping, Data Leaks, and Broadening the Understanding of Sustainable Fashion

Fashion is an embodied form of culture. It builds on intimate information about the wearer, concerning e.g. body shape, size, age, weight and style. As fashion is digitalized, intimate body data is shared in online environments, which raises novel concerns about the privacy of fashion consumers. This is especially visible in online shopping, which has revolutionized fashion retail and data collection practices in fashion, particularly as concerns the secure and fair use of consumer data. We argue that the datafication of fashion should be investigated as part of sustainability discourse, and data fairness be established as a responsibility criterion. We analyzed 32 popular Finnish online clothing stores. Data management was studied by analyzing the online stores’ network traffic to third parties, investigating the transparency of their privacy policy documents and analyzing possible dark patterns in the website’s cookie banners. Our findings raise concerns over data responsibility and privacy in various areas. The analyzed websites had a high number of third-party services, averaging 5.2. Only 17 online stores out of the examined 32 (53%) clearly mentioned that they collected identifying information and product information in their privacy policies, and only five of the stores (16%) had no dark patterns in their cookie banners. Our results also suggest that several brands that are otherwise considered ethical may need to pay more attention to their fair data processing and privacy. This indicates that a wider discussion about data privacy and data handling practices is needed. Fair management of data must be part of the process of evaluating the sustainability of clothing companies.