Data Brokers Research Articles

To Partner or Not to Partner? The Partnership Between Platforms and Data Brokers in Two-Sided Markets

Data have become a vital competitive asset for online advertising platforms, such as Facebook and Snapchat. These platforms often partner with data brokers to enhance targeting capabilities, which raises important consumer privacy concerns. This study develops a game-theoretic model to analyze the economic dynamics of partnerships between competing platforms and a data broker in a two-sided market. Our findings reveal that heightened consumer privacy concerns can encourage platforms to collaborate with data brokers rather than discourage them, as these concerns may strategically soften price competition on the advertiser side. However, when both platforms partner with a data broker, a prisoner’s dilemma may arise, even if the broker significantly improves targeting capabilities. From a policy perspective, such partnerships can harm consumer surplus in a purely ad-sponsored model, where users are not charged. Conversely, they may enhance consumer surplus in a mixed revenue model that combines advertising with subscriptions. These insights suggest that policymakers should consider regulations on platform-data broker partnerships to protect consumer interests, particularly regarding privacy protections that platforms may not prioritize under competitive pressures.

What to Expect When You’re Accessing: An Exploration of User Privacy Rights in People Search Websites

People Search Websites, a category of data brokers, collect, catalog, monetize and often publicly display individuals' personally identifiable information (PII). We present a study of user privacy rights in 20 such websites assessing the usability of data access and data removal mechanisms. We combine insights from these two processes to determine connections between sites, such as shared access mechanisms or removal effects. We find that data access requests are mostly unsuccessful. Instead, sites cite a variety of legal exceptions or misinterpret the nature of the requests. By purchasing reports, we find that only one set of connected sites provided access to the same report they sell to customers. We leverage a multiple step removal process to investigate removal effects between suspected connected sites. In general, data removal is more streamlined than data access, but not very transparent; questions about the scope of removal and reappearance of information remain. Confirming and expanding the connections observed in prior phases, we find that four main groups are behind 14 of the sites studied, indicating the need to further catalog these connections to simplify removal.

Transparency of an Honest Data Broker in Providing Electronic Health Record Data Sufficient for Reuse

Electronic Health Records (EHRs) offer a rich data source for clinical researchers to assess a wide variety of treatments and outcomes. Researchers can use Honest Data Brokers (HDBs) to gain access to EHR data for research. Unfortunately, HDBs’ data analysts can inadvertently overlook nuances in clinical workflows when generating request code for data capture and cause data extraction errors that negatively impact EHR data quality. This paper presents findings from interviews with clinical researchers who have had experience with requesting EHR data from the Regenstrief Institute Data Core (RDC), an HDB with data analysts who provide authorised access to EHR data of nearly 25 million patients in the state of Indiana. Our participants wanted greater transparency when they had questions about the quality of the datasets they received. Participants wanted their data analysts to double check the data in their system, explain how they extracted the data, let them visit the data in their system, and/or view/edit the code used for data extraction. We offer a set of recommendations for HDBs on how to provide greater transparency to clinical researchers about the processes used to generate the EHR data they receive and discuss future directions for research.

Impacts of AI on Music Consumption and Fairness

The algorithmic systems utilized by music streaming services have the potential to positively influence individual choices by promoting new artists, but they are also often accused of perpetuating biases. In this research note, we aim to explore the impact of these platforms’ AI-based algorithms on fairness in music consumption. To address this question, we adopt a multidimensional approach that considers the legal, economic, and algorithmic dimensions of fairness. This approach is applied to our EU Horizon Europe Fair MusE project, which advocates for a fairer music ecosystem. However, it should be noted that we propose a tool to score output (playlists) based on fairness models instead of directly altering the algorithms. Data from end users, data brokers, and open-source databases will inform the model, while the processing of the data is aimed at providing users with insights into algorithmic biases and empower them to influence the output. Acknowledging this aspect, this research note serves as a prelude to highlight the need for increased transparency and explainability of algorithms. Furthermore, we seek to inform policy interventions that promote fairness, particularly regarding data sharing between creators and platform providers. Such interventions would foster trust among stakeholders and benefit both users and businesses.

Achieving Transparency in the Data Broker Industry: A Legal Perspective on Regaining Control under the DPDP Act, 2023

Achieving Transparency in the Data Broker Industry: A Legal Perspective on Regaining Control under the DPDP Act, 2023

Strategic data sales with partial segment profiling

We analyse the incentives of a data broker to sell consumer-level data that enable personalised pricing to compete with firms when only a fraction of consumers — centred around one firm that we label “central” — are profiled. The central firm can potentially benefit from the data more than the rival ones (“peripheral”). We show that the data broker may decide not to sell the dataset to the central firm and instead trade with its peripheral competitors. In particular, we identify a strategic reaction of competitors that want to prevent that data increase competition.

A Blockchain-Driven Smart Broker for Data Quality Assurance of the Tagged Periodic IoT Data in Publisher-Subscriber Model

The Publisher-Subscriber model of data exchange has been a popular method for many Internet-based applications, including the Internet of Things (IoT). A traditional PS system consists of publishers, subscribers, and a broker. The publishers create new data for a registered topic, and the data broker relays the data to the corresponding subscribers. This paper introduces a blockchain-based smart broker for the publisher-subscriber (PS) framework for the IoT network. As IoT data comes from devices operating in various environments, it may suffer from multiple challenges, such as hardware failures, connectivity issues, and external vulnerabilities, thereby impacting data quality in terms of accuracy and timeliness. It is important to monitor this data and inform subscribers about its quality. The proposed smart broker is composed of multiple smart contracts that continuously monitor the quality of the topic data by assessing its relationship with other related topics and its drift or delay in publishing intervals. It assigns a reputation score to each topic computed based on its quality and drifts, and it passes both the original data and the reputation score as a measure of quality to the subscriber. Furthermore, the smart broker can suggest substitute topics to subscribers when the requested topic data are unavailable or of very poor quality. The evaluation shows that a smart broker efficiently monitors the reputation of the topic data, and its efficiency increases notably when the data quality is worse. As the broker is run inside the blockchain, it automatically inherits the advantages of the blockchain, and the quality scoring is indisputable based on immutable data.

Regulating data sales: The role of data selling mechanisms

We analyze the effects of different data selling mechanisms of a monopolistic Data Broker (DB) who sells consumer data to firms in a downstream market with free entry, where data can be used for consumer price discrimination. We consider three possible data selling mechanisms, namely auctions with and without reserve prices, and Take-It-Or-Leave-It offers, which exhibit decreasing levels of DB’s bargaining power towards firms. We highlight the emergence of an entry barrier effect in the downstream market, regardless of the data selling mechanism. Moreover, we show that the auction-based selling mechanisms, and particularly the auction with reserve prices, induce the DB to sell the lowest quantity of data, implying the lowest level of consumer surplus. Conversely, under TIOLI, the DB floods the market for data, selling to all firms data partitions that overlap over subsets of consumers. Imposing the sale of non-overlapping partitions to all firms would improve consumer surplus and welfare.

“Whispers from the Wrist”: Wearable Health Monitoring Devices and Privacy Regulations in the U.S.: The Loopholes, the Challenges, and the Opportunities

The growth of wearable technology has enabled the collection of even more personalized information on individuals. New health-related devices marketed to consumers collect health information that might not fall under the traditional category of Protected Health Information, and thus, HIPAA protections do not fully apply. Meaning, commercial wearable health devices do not fall under FDA oversight, and data not paired with a doctor–patient relationship do not fall under HIPAA privacy protection; thus, much of the gathered health-related metrics are left without regulation and open to be sold to data brokers. As such, these data can be leveraged by health insurance, law enforcement, and employers, to name a few. This manuscript explores the loopholes in current regulations and suggests a framework that categorizes wearable data and addresses challenges in data transfer. Furthermore, taking a user perspective, the suggested framework offers solutions that aim to guide users and policymakers in navigating privacy issues in wearable technology.

Intrusion detection in machine learning based E-shaped structure with algorithms, strategies and applications in wireless sensor networks

In the everyday world of computer applications, from the cloud to the Internet of Things, distributed sensor networks are essential (IoT). These computer application devices are often connected to Arduino network connection and microcontrollers such sensors and actuators. Thus, a defensive network with an IDS serves as the need for contemporary networks. The intrusion detection system has unavoidably evolved throughout the years, but despite this, it remains a difficult study topic since the current intrusion detection system uses signature-based approaches rather than anomaly detection. Therefore, improving the current intrusion detection system is challenging since it is difficult to find zero-day attacks in IoT networks when dealing with varied data sources. Filtered Deep Learning Model for Intrusion Detection with a Data Communication Approach is presented in this study. The five steps that make up the suggested model are Initialization of Sensor Networks, Cluster Formation and Head Selection, Connectivity, Attack Detection, and Data Broker. It was discovered that the suggested model for intrusion detection outperformed both the current Deep Learning Neural Net and Artificial Neural Network. In comparison to the most popular algorithms, experimental findings revealed a superior result of 96.12 % accuracy. The E-shaped patch antenna is a brand-new single-patch wide-band microstrip antenna that is presented in this research. A microstrip antenna's patch has two parallel slots built into it to increase its bandwidth. Investigating the behaviour of the currents on the patch allows for the exploration of the wide-band mechanism. A broad bandwidth is achieved by optimising the slot's length, breadth, and location. Finally, a 40.3 % E-shaped patch antenna is developed, made, and tested to resonate at 7.5 and 8.5 GHz for wireless communications. Additionally displayed are the reflection coefficient, VSWR, radiation pattern and directivity.

Mediating educational technologies: Edtech brokering between schools, academia, governance, and industry

The use of educational technologies in schools is being reshaped by a new kind of intermediary organization that brokers relations between schools, academia, governance, and industry. In this article we define and examine ‘edtech brokers’ as organizations that operate between the edtech industry, public schools, research centers and governments, guiding schools in the procurement and pedagogical use of edtech. Edtech brokers have remained mostly unexplored despite their potential to redraw the boundaries between public education and the global edtech market. We claim that edtech brokers have become increasingly relevant in the past years, embedding new types of professionalities into education, and taking an active role in co-creating and updating schools’ digital infrastructures, the evidence-making mechanisms around edtech, and the pedagogical practices around edtech. The article proposes three distinct categories of edtech brokers – ambassador, search engine, and data brokers – and explores their practices of mediation. By doing so, we outline the potential effects that brokers can have on schools and edtech markets, and we disentangle their specific imaginaries of the future of education they promote, often aligned with wider policy desires for reform.

Blessing or Curse? Implications of Data Brokers for Publisher Competition

The explosion of consumer data has spawned a burgeoning data broker industry, pivotal in targeted advertising. A recent media report estimates the worth of the data broker industry by 2030 at approximately $382 billion. Key players such as Oracle and Lotame gather data from diverse sources to create and sell valuable insights. Digital publishers often rely on these brokers to gain (i) individual insights drawn from their own data or (ii) collective insights drawn from both their data and those of their competitors, improving their targeting precision. This study develops an analytical model to explore the competitive implications of data brokers in a targeted advertising market with two competing publishers and a mass of advertisers. It reveals that the data broker might strategically price their insights to exclusively offer collective insights to one publisher, thereby altering market competition. Moreover, the broker’s actions can either foster or hinder competition among publishers, depending on its strategic interests. Despite potentially reducing market competition through exclusive selling, the provision of collective insights can enhance aggregate welfare by enhancing publishers’ targeting capability. This illustrates the nuanced interplay between data brokers, publishers, and advertisers in shaping the landscape of targeted advertising.

Athlete Health and Human Performance Will Not Improve Without Transdisciplinary Collaboration and Data Sharing in Elite Sport.

There are two largely competing models for an athletics organization at both the collegiate and professional levels: the High Performance Model and the Medical Model. The High Performance Model arises from international Football perspective that places a "Performance Director" at the center of teams supporting the athletes. The Medical Model, supported by both the National Athletic Trainers Association and the NCAA, separates off medical staff (athletic trainers, physical therapists, and physicians, predominantly) and emphasizes the autonomy of medical decisions. The Medical Model has left clinicians in a "medical silo", limiting our ability to care for the individual athletes as holistic people and limits our wider impact in the field of athlete health and injury mitigation. We argue that Medical. Model is consistent with the High Performance Model only if we reject the notion that the "Performance Director" is an administrative person and instead conceptualize this as a "Health and Performance Information Hub" which facilitates transdisciplinary collaboration. This Commentary details how a data broker system can be used to accelerate transdisciplinary collaboration within an athletic organization, leading to better healthcare for athletes and improved team and individual performance. Furthermore, a transdisciplinary organization with data sharing is able to turn real-world data into real-world evidence, enhancing the care and performance of athletes locally as well as facilitating the creation of generalizable knowledge in the area of sports medicine and human performance.

Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic.

The COVID-19 pandemic prompted new interest in non-traditional data sources to inform response efforts and mitigate knowledge gaps. While non-traditional data offers some advantages over traditional data, it also raises concerns related to biases, representativity, informed consent and security vulnerabilities. This study focuses on three specific types of non-traditional data: mobility, social media, and participatory surveillance platform data. Qualitative results are presented on the successes, challenges, and recommendations of key informants who used these non-traditional data sources during the COVID-19 pandemic in Spain and Italy. A qualitative semi-structured methodology was conducted through interviews with experts in artificial intelligence, data science, epidemiology, and/or policy making who utilized non-traditional data in Spain or Italy during the pandemic. Questions focused on barriers and facilitators to data use, as well as opportunities for improving utility and uptake within public health. Interviews were transcribed, coded, and analyzed using the framework analysis method. Non-traditional data proved valuable in providing rapid results and filling data gaps, especially when traditional data faced delays. Increased data access and innovative collaborative efforts across sectors facilitated its use. Challenges included unreliable access and data quality concerns, particularly the lack of comprehensive demographic and geographic information. To further leverage non-traditional data, participants recommended prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations. The value of non-traditional data was perceived as underutilized in public health surveillance, program evaluation and policymaking. Participants saw opportunities to integrate them into public health systems with the necessary investments in data pipelines, infrastructure, and technical capacity. While the utility of non-traditional data was demonstrated during the pandemic, opportunities exist to enhance its impact. Challenges reveal a need for data governance frameworks to guide practices and policies of use. Despite the perceived benefit of collaborations and improved data infrastructure, efforts are needed to strengthen and sustain them beyond the pandemic. Lessons from these findings can guide research institutions, multilateral organizations, governments, and public health authorities in optimizing the use of non-traditional data.

User Data and Endogenous Entry in Online Markets*

We investigate how the presence of a Data Broker (DB), who sells consumer data to downstream firms, affects firm entry and competition in a horizontally differentiated oligopoly market, in which data allow firms to price discriminate. The DB chooses the price and amount of data sold to each firm. We show that the data sale by the DB reduces excessive market entry, as the competition induced by personalized prices exerts a downward pressure on prices and profits. The data‐induced entry barrier and resulting weakened competition dominates the pro‐competitive effect of personalized prices. Consequently, while the DB's presence might alleviate excessive market entry, it also diminishes consumer surplus.

Development of a model of personal data protection in the context of digitalization of the educational sphere using information technology tools

The paper shows that at the present stage of digitalization development, the issue of personal data protection in the educational sphere by means of information technology tools is important. Based on the analysis of information interactions in the educational environment, the author identifies the categories of parties (subjects) which may be affected by the loss of personal data in such interactions in the context of digitalization. The most vulnerable category to the loss of personal data is identified – they are so called- eduction searchers. The main objects involved in the exchange of information in the educational sphere are presented. The application of the expert method to determine the priority of the object of information interactions is proposed. It is established that this is the "Data Broker". Based on the information obtained, a model of information interaction in the educational environment in the context of digitalization and a model for determining the loss of personal data during digitalized information integration in the field of education have been developed. Prioritised subjects in such integrations are identified. A model for protecting personal data of educational stakeholders in the context of digitalization with the help of information technology tools has been developed. The basic requirements for an information tool are highlighted. The developed model will make it possible to develop the issues of information protection, confidentiality, integrity and availability of data in the context of digitalization of the educational sphere.

Formal Comments on Docket No. CFPB-2023-0020: Request for Information Regarding Data Brokers and Other Business Practices Involving the Collection and Sale of Consumer Information

Formal Comments on Docket No. CFPB-2023-0020: Request for Information Regarding Data Brokers and Other Business Practices Involving the Collection and Sale of Consumer Information

TOWARD A REVOLUTION IN AUSTRALIAN CHILDREN’S DATA AND PRIVACY

This panel combines four papers which focus in different ways on the question of children’s data and privacy in the Australian context. All four are framed with children’s right to privacy as a core concern, consistent with the UN Convention on the Rights of the Child as updated via the General Comment 25 on Child Rights in the Digital Environment. We examine four arenas where children’s data is either extracted or occluded in ways that make it more difficult, if not impossible, for parents, carers and others to make informed choices about the data of very young children. As children begin to articulate their own ideas and privacy preferences, these studies highlight different understandings of privacy, and of trust in both people and technologies. The panel papers are titled: ‘Where Does Children’s Data Go? Mapping the Data Broker Industry’; ‘Data and Privacy as a Social Relation’; ‘Developing a Holistic Framework for Analysing Privacy Policies – A Child’s Rights and Data Justice Perspective’ and ‘Unboxing Data and Privacy Via Young Children’s Wearables’. Collectively, these papers can be read as arguing that we need nothing less than a revolution in the way children and responsible adults are informed about the way children’s data is generated, captured, stored, and owned, as well as explicitly regulating who can profit from children’s data, in which circumstances, and how transparent these processes must be.

A Profit Maximization Model for Data Consumers with Data Providers’ Incentives in Personal Data Trading Market

This paper proposes a profit maximization model for a data consumer when it buys personal data from data providers (by obtaining consent) through data brokers and provides their new services to data providers (i.e., service consumers). To observe the behavioral models of data providers, the data consumer, and service consumers, this paper proposes the willingness-to-sell model of personal data of data providers (which is affected by data providers’ behavior related to explicit consent), the service quality model obtained by the collected personal data from the data consumer’s perspective, and the willingness-to-pay model of service consumers regarding provided new services from the data consumer. Particularly, this paper jointly considers the behavior of data providers and service users under a limited budget. With parameters inspired by real-world surveys on data providers, this paper shows various numerical results to check the feasibility of the proposed models.

Use of mobile digital footprint to understand how COVID-19 impacts the visitors to art places in Singapore

As Singapore strengthens its position as a Creative City, the importance of art places is increasingly evident. The time and frequency of visitors spent on the art places are indications of how successful the art places are. The understanding of the footfall for art places using traditional methods, such as ticket sales, and person-count can be expensive and inaccurate. With the prevalence of mobile phone adoption, our digital footprints are silently collected. These digital footprints can be used to understand the number of visitors who have visited art places, as well as their time spent at the different art places. This data is collected by CITYDATA.ai, a geospatial AI platform and registered data broker compliant with global and regional privacy regulations. An interesting but almost unobserved trend is to understand how COVID-19 affects the visitors to art places. During this period, there could also be a fundamental shift in how people feel and react towards art places. The contribution of this paper helps planners to understand the dynamics of change for the visitors to art places and subsequently enables the planners to have the right strategies to reposition Singapore as a Creative City.