Consumer Privacy Research Articles

Impact of Unmet Privacy Expectations Across Information, Time, and Space: Evidence from Four Countries

Globally, consumers and firms are negotiating rising privacy expectations. This article advances research by developing a multidimensional privacy measure that captures the idea of privacy as an expectation to be left alone through control and access of their personal information, time, and space. Drawing on boundary regulation perspectives from law and environmental psychology, the authors conceptualize, develop, and measure the meeting privacy expectations (MPE) scale. Six studies show the reliability and stability of the 12-item MPE scale in depicting the three subdimensions and in meeting reliability and validity criteria. The results indicate that the MPE scale is not only distinct from previously established privacy scales but also a better predictor of behavioral intentions. Moreover, the scale is invariant across different cultures and influences cognitive trust and emotional violation. The authors find that meeting privacy expectations vary in their levels and outcomes cross-culturally. Marketers can use the MPE scale to globally track, assess, manage, and plan for meeting the changing consumer privacy expectations. The MPE scale is a good candidate to address the rising public policy issues related to privacy.

On the Legal Regulation Dilemma of Network Platform Using Big Data to Infringe Citizens' Privacy——From the Perspective of Big Data Murder

With the popularization of computers and smart phones, the number of Internet users in China is increasing, and network technology is also developing. The resulting big data and related technologies have brought a series of economic benefits. However, there are some network platforms that only seek profits and ignore business ethics. They use big data to kill users.There are different opinions on the concept of big data in China. However, there is no doubt that the network platform using big data murder seriously violates the privacy of consumers, and causes consumers to suffer property losses, resulting in adverse social impact.Although there are many laws to protect citizens' privacy in our country, there is still a dilemma of legal regulation for the infringement of citizens' privacy by network platform and the use of big data. The reason is that big data as a new technology, the original law inevitably lags behind; moreover, due to its high technical requirements, ordinary citizens have great difficulties in perception and proof of big data familiarity killing due to information asymmetry.

Enhancing consumer perceptions of privacy and trust: a GDPR label perspective

PurposeThe General Data Protection Regulation (GDPR) introduces significant data protection obligations on all organizations within the European Union (EU) and those transacting with EU citizens. This paper presents the GDPR privacy label and uses two empirical studies to examine the effectiveness of this approach in influencing consumers' privacy perceptions and related behavioral intentions.Design/methodology/approachThe paper tests the efficacy of two GDPR privacy label designs, a consent-based label and a static label. Study 1 examines the effects of each label on perceptions of risk, control and privacy. Study 2 investigates the influence of consumers' privacy perceptions on perceived trustworthiness and willingness to interact with the organization.FindingsThe findings support the potential of GDPR privacy labels for positively influencing perceptions of risk, control, privacy and trustworthiness and enhancing consumers' willingness to transact and disclose data to online organizations.Practical implicationsThe findings are useful for organizations required to comply with the GDPR and present a solution to requirements for transparent communications and explicit consent.Originality/valueThis study examines and demonstrates the efficacy of visualized privacy policies in impacting consumer privacy perceptions and behavioral intentions.

Anonymization of distribution feeder data using statistical distribution and parameter estimation approach

Distribution networks are undergoing a transition due to the rapid increase in distributed energy resources (DERs). Concerns over revealing critical infrastructure information and breaching consumer privacy have significantly hindered their development. Many applications that require data sharing are still impractical due to the lack of sufficient datasets, privacy concerns, and data access problems. To address these issues, a statistical distribution approach for parameter estimation is proposed. The statistical patterns of real distribution feeders are examined by accessing the confidential database of a local distribution network service provider (DNSP). An algorithm based on the Maximum Likelihood Estimate (MLE) is applied to estimate the statistical distribution parameters that represent the actual data. Then, these statistical distribution parameters are used to generate anonymized datasets that are realistic. A Kolmogorov–Smirnov (K-S) test is conducted to confirm the effectiveness of anonymized datasets, and results are compared with the actual feeder datasets. Validation is carried out with existing methods and comparisons are shown on the different portions of the datasets (25 percent, 50 percent, 75 percent, and 100 percent). The comparison results indicate superior performance over traditional methods, with a performance improvement ranging from 1 to 13 percent. The practical application of the method is demonstrated on the IEEE 123-node test feeder. The method achieves consistent results on voltage profiles, with a maximum difference of 0.420 percent between actual and anonymized datasets.

Fed‐NILM: A federated learning‐based non‐intrusive load monitoring method for privacy‐protection

Non-intrusive load monitoring (NILM) is essential for understanding consumer power consumption patterns and may have wide applications such as in carbon emission reduction and energy conservation. Determining NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners when determining the NILM model. To address these problems, a novel NILM method based on federated learning (FL) called Fed-NILM is proposed. In Fed-NILM, instead of local load data, local model parameters are shared among multiple data owners. The global NILM model is obtained by averaging the parameters with the appropriate weights. Experiments based on two measured load datasets are performed to explore the generalization capability of Fed-NILM. In addition, a comparison of Fed-NILM with locally trained NILM models and the centrally trained NILM model is conducted. Experimental results show that the Fed-NILM exhibits superior performance in terms of scalability and convergence. Fed-NILM out performs locally trained NILM models operated by local data owners and approaches the centrally trained NILM model, which is trained on the entire load dataset without privacy protection. The proposed Fed-NILM significantly improves the co-modelling capabilities of local data owners while protecting the privacy of power consumers.

Consumer privacy and distribution contract

PurposeIn the Information Age, an increasing number of firms and researchers focus on consumer privacy. Meanwhile, many firms that collect consumer information through, information disclosure, consumer privacy, agency model, distribution contracts products or services often adopt the agency contract or the wholesale contract to sell through the online platform. This study aims to examine how different distribution contracts affect supply chain decisions when the firm can profit from disclosing consumer information.Design/methodology/approachThe authors use Stackelberg model to describe the relationship between consumer privacy and distribution contracts. Solve the model and analyze the monotonicity of the equilibrium results. The optimal contract choice and win-win conditions are obtained by comparing the profits under different contracts.FindingsThe authors find that when consumers’ maximal valuation is low in the market, the firm prefers to profit from disclosing consumer information under both the agency contract and the wholesale contract. As consumers’ maximal valuation increases, the firm turns to profit from product sales. Under the agency contract, the platform only generates profit when the consumers’ maximal valuation is high. By comparing the profits of the platform under the two types of contracts, the authors find the platform’s optimal contract choice under different consumers’ maximal valuations and platform commission rates. Combined with the comparison results of the firm’s profit, the authors provide the win-win conditions under the agency contract and wholesale contract.Originality/valueThis study analyzes the supply chain decision under the agency contract and wholesale contract, and it helps deepen the understanding of the interaction between consumer information disclosure and channel distribution contract.

The Impact of Artificial Intelligence on Sustainable Development in Electronic Markets

With the emergence of artificial intelligence (AI), the technological revolution has transformed human lives and processes, empowering the products and services in today’s marketplaces. AI introduces new ways of doing jobs and business, and of exploring new global market opportunities. However, on the other hand, it provides many challenges to comprehend. Therefore, our study’s main objective was to examine the behavioral, cultural, ethical, social, and economic challenges of AI-enabled products and services in consumer markets and discuss how businesses might shape their approaches to address AI-related ethical issues. AI offers numerous social, ethical, and behavioral difficulties for people, organizations, and societies that endanger the sustainable development of economies. These fundamental challenges due to AI technologies have raised serious questions for the sustainable development of electronic markets. Based on this, the current study presents a framework highlighting these issues. Systematic reviewing was our research method; we looked for explicit information and methods that indicate the credibility of research or reduce biases. This paper is of great importance, as it highlights several behavioral, societal, ethical, and cultural aspects in electronic markets which were not presented in previous studies. Some key issues are the security and privacy of consumers, AI biases, individual autonomy, wellbeing, and issues of unemployment. Therefore, companies that use AI systems need to be socially responsible and make AI systems as secure as possible to promote the sustainable development of countries. The results suggest that AI has undoubtedly transformed life and has both positive and negative effects. However, the main aim regarding AI should be to use it for the overall goals of humanity. Moreover, authorities operating in e-business environments need to create proper rules and regulations and make the systems as secure as possible for people.

Socially-Responsible Target Marketing and Marketing Communications

Companies are in business to generate revenues and profits and achieve business excellence. Now-a-days, companies are becoming sensitive towards their social responsibilities of ensuring not only consumer welfare but also the welfare of all stakeholders including the society at large. Companies do target marketing and marketing communications to communicate about their offerings to target markets. They adopt socially responsible target marketing, marketing communications, and advertising campaigns. Various issues in advertising campaigns include deceptive advertising, and unethical practices in fashion advertising and in cosmetics industry. Companies adopt advertising campaigns for social causes, socially responsible sales promotion, and personal selling. Companies should ensure consumer privacy, safety, and security. Various stakeholders want to be assured that businesses care about their welfare. Businesses should ensure consumer welfare and welfare of the society at large. This will allow them to achieve business excellence and stay ahead in the competition.

An Efficient Blockchain Based Data Access with Modified Hierarchical Attribute Access Structure with CP-ABE Using ECC Scheme for Patient Health Record

Secure patient health record (PHR) information exchange via cloud computing is a considerable security risk to user privacy. The fundamental reason of this issue is cloud computing’s reliance on trustworthy third parties to share data across it. To exchange data securely, many conventional cryptographic algorithms employ various keying approaches. However, relying on a trusted third party compromises the privacy of consumers’ data. To offer secure communication without the involvement of a third party, a distributed blockchain based (DBC) ciphertext–policy attribute-based encryption (CP-ABE) approach is employed in this study. Because of bilinear paring and simple scalar multiplication factors, the proposed CP-ABE system is entirely dependent on elliptic curve cryptography to reduce complexity. Furthermore, the data requester provides dynamic attributes, and a user-centric access policy is created, allowing multiple authorities to manage the attributes and provide data access. Data confidentiality, data authentication, user authentication, and tamper-proof data are all guaranteed by the suggested method. The DBC-CP-ABE method is used to provide user-centric access policies and effective key management.

System Error: Where Big Tech Went Wrong and How We Can Reboot

System Error: Where Big Tech Went Wrong and How We Can Reboot

An Overview of Smartphone Apps


 Health-based mobile applications (mHealth) are downloadable applications on a smartphone or similar device for use in health care, either by the person directly or by a health care provider. This Horizon Scan summarizes the available information and provides an overview of health apps on smartphones that are not connected to specialized medical equipment, describing examples of emerging apps in different clinical areas, who they might benefit, and their operational issues.
 There are over 350,000 mobile applications available for download in app stores, used for a variety of disease areas. These areas include chronic disease, stress, mental health, fitness, sleeping problems, general medication adherence and tracking, and vital sign measurements.
 The scan identified that health apps often fall into 1 of 4 categories: informational applications, diagnostic applications, disease management applications, and fitness tracking applications.
 Numerous apps were identified that are available for use by people in Canada. Health apps offer the potential to provide convenience, flexibility, accessibility, and personalized health information. However, the majority of health apps that require evidence of benefit for users have not been assessed in appropriately designed studies that examine their clinical efficacy, or safety. The apps that have been tested in some research studies may have numerous shortcomings in areas such as app design, user engagement, user satisfaction, and retention.
 This scan describes some operational considerations for apps that relate to their lack of evidence-base, concerns about biases in app design, and the need for equity focused app development. It was noted in the literature that many apps do not provide appropriate privacy and confidentiality for consumers, which may put people at risk of data breaches or inappropriate use of personal data.

Net Zero and the potential of consumer data - United Kingdom energy sector case study: The need for cross-sectoral best data practice principles

The nationwide rollout of smart meters marks a significant milestone in the digitalisation of the UK energy sector. It has allowed the collection of unprecedented amounts of consumer energy consumption and behavioural data. Providing real-time, spatially explicit, bi-directional connectivity between service providers and consumers, this data is expected to provide benefits of over £40B and contribute to behavioural nudges - which are integral to 62% of all initiatives required to achieve Net Zero by 2050. Concurrently, consumers are leaving behind digital trails across a broader spectrum of their lives through their smartphones and in-home devices. This data could also be used to accelerate digitalisation within the energy sector and yet the energy sector has limited or no visibility of it. While greater access to consumer data is expected to provide substantial opportunities for economic growth and the realisation of Net Zero both in the energy sector and across the UK economy, it also risks consumer exploitation.This policy perspectives seeks to provide insights from data and digital specialists in the UK rather than just actors within the energy sector. It uses psychographics as a use case to highlight the tension where data, particularly consumer data, is under regulated and siloed across the UK economy. The potential to use consumer data to change consumer behaviour to enable net zero opportunities is explored.The analysis strongly indicates that the ability for high fidelity consumer data to elicit behavioural insight is expected to become an increasingly valuable tool to inform both policy and business decisions in the energy transition - particularly around innovation and enhanced competition. These opportunities are balanced by the fact that high-definition consumer data collection practices are already perceived as exploitative. The lack of data sharing infrastructure across the economy has allowed incumbent technology companies to establish monopoly power thereby stifling competition and raising barriers to entry. The analysis makes a series of high-level recommendations around cross-economy data principles to enhance sharing and stimulate innovation whilst protecting consumer privacy to build digital trust. Options as to how these might be realised are also proposed and should form the basis of future research.

Pulse Oximeter App Privacy Policies During COVID-19: Scoping Assessment

BackgroundPulse oximeter apps became of interest to consumers during the COVID-19 pandemic, particularly when traditional over-the-counter pulse oximeter devices were in short supply. Yet, no study to date has examined or scoped the state of privacy policies and notices for the top-rated and most downloaded pulse oximeter apps during COVID-19.ObjectiveThe aim of this study was to examine, through a high-level qualitative assessment, the state and nature of privacy policies for the downloaded and top-rated pulse oximeter apps during the COVID-19 pandemic to (1) compare findings against comparable research involving other mobile health (mHealth) apps and (2) begin discussions on opportunities for future research or investigation.MethodsDuring August-October 2020, privacy policies were reviewed for pulse oximeter apps that had either at least 500 downloads (Google Play Store apps only) or a three out of five-star rating (Apple Store apps only). In addition to determining if the apps had an accessible privacy policy, other key privacy policy–related details that were extracted included, but were not limited to, app developer location (country); whether the app was free or required paid use/subscription; whether an ads disclosure was provided on the app’s site; the scope of personal data collected; proportionality, fundamental rights, and data protection and privacy issues; and privacy safeguards.ResultsSix pulse oximeter apps met the inclusion criteria and only 33% (n=2) of the six apps had an accessible privacy policy that was specific to the pulse oximeter app feature (vs the app developer’s website or at all). Variation was found in both the regulatory nature and data privacy protections offered by pulse oximeter apps, with notable privacy protection limitations and gaps, although each app provided at least some information about the scope of personal data collected upon installing the app.ConclusionsPulse oximeter app developers should invest in offering stronger privacy protections for their app users, and should provide more accessible and transparent privacy policies. This is a necessary first step to ensure that the data privacy of mHealth consumers is not exploited during public health emergency situations such as the COVID-19 pandemic, where over-the-counter personal health monitoring devices could be in short supply and patients and consumers may, as a result, turn to mHealth apps to fill such supply gaps. Future research considerations and recommendations are also suggested for mHealth technology and privacy researchers who are interested in examining privacy implications associated with the use of pulse oximeter apps during and after the COVID-19 pandemic.

Privacy considerations for online advertising: a stakeholder’s perspective to programmatic advertising

PurposePrivacy considerations have become a topic with increasing interest from academics, industry leaders and regulators. In response to consumers’ privacy concerns, Google announced in 2020 that Chrome would stop supporting third-party cookies in the near future. At the same time, advertising technology companies are developing alternative solutions for online targeting and consumer privacy controls. This paper aims to explore privacy considerations related to online tracking and targeting methods used for programmatic advertising (i.e. third-party cookies, Privacy Sandbox, Unified ID 2.0) for a variety of stakeholders: consumers, AdTech platforms, advertisers and publishers.Design/methodology/approachThis study analyzes the topic of internet user privacy concerns, through a multi-pronged approach: industry conversations to collect information, a comprehensive review of trade publications and extensive empirical analysis. This study uses two methods to collect data on consumer preferences for privacy controls: a survey of a representative sample of US consumers and field data from conversations on web-forums created by tech professionals.FindingsThe results suggest that there are four main segments in the US internet user population. The first segment, consisting of 26% of internet users, is driven by a strong preference for relevant ads and includes consumers who accept the premises of both Privacy Sandbox and Unified ID (UID) 2.0. The second segment (26%) includes consumers who are ambivalent about both sets of premises. The third segment (34%) is driven by a need for relevant ads and a strong desire to prevent advertisers from aggressively collecting data, with consumers who accept the premises of Privacy Sandbox but reject the premises of UID 2.0. The fourth segment (15% of consumers) rejected both sets of premises about privacy control. Text analysis results suggest that the conversation around UID 2.0 is still nascent. Google Sandbox associations seem nominally positive, with sarcasm being an important factor in the sentiment analysis results.Originality/valueThe value of this paper lies in its multi-method examination of online privacy concerns in light of the recent regulatory legislation (i.e. General Data Protection Regulation and California Consumer Privacy Act) and changes for third-party cookies in browsers such as Firefox, Safari and Chrome. Two alternatives proposed to replace third-party cookies (Privacy Sandbox and Unified ID 2.0) are in the proposal and prototype stage. The elimination of third-party cookies will affect stakeholders, including different types of players in the AdTech industry and internet users. This paper analyzes how two alternative proposals for privacy control align with the interests of several stakeholders.

Starlinked! An analysis of SpaceX’s small satellite mega-constellation under the Fourth Amendment

ABSTRACT Private commercialization is making broadband internet accessible through a new frontier: space. Space Exploration Holdings, LLC (SpaceX), is launching a mega-constellation of thousands of small-satellites into low-Earth orbit to provide fixed-satellite service across the globe. In order to do so, SpaceX was required to receive a license from the Federal Communications Commission (FCC). The licensing regime is part of a larger international regulatory framework based on five governing treaties – the Outer Space Treaty, Rescue Agreement, Liability Convention, Registration Convention, and the Moon Treaty. Compliance with these treaties is managed through the International Telecommunications Union (ITU) an agency of the United Nations. In this instance, the FCC’s domestic licensing processes ensure compliance by the United States to the ITU’s procedures and the treaties. The role of the FCC in maintaining these regulatory operations may, however, introduce risks to consumer privacy. This paper explores the tension between government regulation of information and communication technologies, specifically, small satellite mega-constellations and the right to privacy under the Fourth Amendment of the United States Constitution.

Legal, Marketing, and Advertising Issues with Big Data

The purpose of this essay is to discuss the advantages and disadvantages and the benefits and costs of Big Data. The paper outlines the relevant federal and state privacy laws, including the California Consumer Privacy Act as amended, the Virginia Consumer Data Protection Act, and the Colorado Privacy Act. While highlighting several Federal Trade Commission privacy violation cases, the effects of Big Data collection and government surveillance are described in some detail. Advertising and marketing are defined, where it is argued that while the scanning of emails by email providers may be legal, it should be accomplished with consent, or not at all. The essentials of contract law and specific contract negotiation techniques are outlined for the benefit of attorneys. Finally, it is argued that although Big Data is the wave of the future, like all human institutions, it has in its definition the inherent paradoxes of transparency, identity, power, and exclusion that may potentially spell its undoing.

Mental Health Mobile Apps and the Need to Update Federal Regulations to Protect Users

With greater societal emphasis on the need for better mental health services coupled with COVID-19 limits, mental health mobile applications have significantly risen in variety, availability, and accessibility. As more consumers use mental health mobile applications, more data is generated and collected by mobile application companies. However, consumers may have the false assumption that the data collected is protected under HIPAA or have an expectation of privacy protection higher than current regulations afford. This Note examines HIPAA, Health Breach Notification Rule, and section 5 of the Federal Trade Commission Act, as well as how these regulations fall short of protecting the data and privacy of consumers who use mental health mobile apps. This Note then advocates for a preventative approach by Congress towards potential data breaches and protection of data from mental health mobile apps. Looking prospectively, the Note suggests how the gaps in consumer protection can be federally remedied.

Consumer's Privacy Calculus for Iot Services and the Role of Privacy Awareness

Consumer's Privacy Calculus for Iot Services and the Role of Privacy Awareness

Guarding online privacy: Privacy seals and government regulations

The proliferation of the Internet as a business medium has exacerbated violation of individual privacy. New e-business technologies have increased the ability of online merchants to collect, monitor, target, profile, and even sell personal information about consumers to third parties. Governments, business houses and employers collect data and monitor people, but their practices often threaten an individual's privacy. Because vast amount of data can be collected on the Internet and due to global ramifications, citizens worldwide have expressed concerns over increasing cases of privacy violations. Several privacy groups, all around the world, have joined hands to give a boost to privacy movement. Consumer privacy, therefore, has attracted the widespread attention of regulators across the globe. With the European Directive already in force, “trust seals ” and “government regulations” are the two leading forces pushing for more privacy disclosures. Of course, privacy laws vary throughout the globe but, unfortunately, it has turned out to be the subject of legal contention between the European Union and the United States. The EU has adopted very strict laws to protect its citizens’ privacy, in sharp contrast, to ‘lax-attitude’ and ‘self-regulated’ law of the US. For corporations that collect and use personal information, now ignoring privacy legislative and regulatory warning signs can prove to be a costly mistake. An attempt has been made in this paper to summarize the privacy legislation prevalent in Australia, Canada, the US, the EU, India and Japan. It is expected that a growing number of countries will adopt privacy laws to foster e-commerce.

Mitigating consumer privacy breach in smart grid using obfuscation-based generative adversarial network.

Smart meters allow real-time monitoring and collection of power consumption data of a consumer's premise. With the worldwide integration of smart meters, there has been a substantial rise in concerns regarding threats to consumer privacy. The exposed fine-grained power consumption data results in behaviour leakage by revealing the end-user's home appliance usage information. Previously, researchers have proposed approaches to alter data using perturbation, aggregation or hide identifiers using anonymization. Unfortunately, these techniques suffer from various limitations. In this paper, we propose a privacy preserving architecture for fine-grained power data in a smart grid. The proposed architecture uses generative adversarial network (GAN) and an obfuscator to generate a synthetic timeseries. The proposed architecture enables to replace the existing appliance signature with appliances that are not active during that period while ensuring minimum energy difference between the ground truth and the synthetic timeseries. We use real-world dataset containing power consumption readings for our experiment and use non-intrusive load monitoring (NILM) algorithms to show that our approach is more effective in preserving the privacy level of a consumer's power consumption data.