Privacy's Peril: Unmasking the Unregulated Underground Market of Data Brokers and the Suggested Framework

• URL: http://arxiv.org/abs/2410.04606v1
• Date: Sun, 6 Oct 2024 19:51:31 GMT
• Title: Privacy's Peril: Unmasking the Unregulated Underground Market of Data Brokers and the Suggested Framework
• Abstract summary: In part I, we will look at what a data broker is, how it collects information, the data industry, and some of the difficulties it faces. In part II, we will look at potential options for regulating data brokers. All options are provided in light of the EU General Data Protection Regulation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The internet is a common place for businesses to collect and store as much client data as possible and computer storage capacity has increased exponentially due to this trend. Businesses utilize this data to enhance customer satisfaction, generate revenue, boost sales, and increase profile. However, the emerging sector of data brokers is plagued with legal challenges. In part I, we will look at what a data broker is, how it collects information, the data industry, and some of the difficulties it encounters. In Part II, we will look at potential options for regulating data brokers. All options are provided in light of the EU General Data Protection Regulation (GDPR). In Part III, we shall present our analysis and findings.

Related papers

• Privacy Without Remedy: An Assessment of Data Broker Compliance with California Privacy Law [4.524476021202175]
  We offer the first empirical assessment of data broker compliance with the California Consumer Privacy Act and the 2023 Delete Act.<n>We show that only 9% of registered data brokers were fully compliant with transparency requirements after the Delete Act took effect.<n>We articulate reforms that could improve consumer privacy, transparency in broker practices, and compliance with these laws.
  arXiv  Detail & Related papers  (2026-05-20T16:39:19Z)
• From "I have nothing to hide" to "It looks like stalking": Measuring Americans' Level of Comfort with Individual Mobility Features Extracted from Location Data [0.24999074238880484]
  Location data collection has become widespread with smart phones becoming ubiquitous. Data aggregators and data brokers offer access to individual location data. The FTC has also started to vigorously regulate consumer privacy for location data.
  arXiv  Detail & Related papers  (2025-02-08T20:34:18Z)
• Towards Data Governance of Frontier AI Models [0.0]
  We look at how data can enable new governance capacities for frontier AI models. Data is non-rival, often non-excludable, easily replicable, and increasingly synthesizable. We propose a set of policy mechanisms targeting key actors along the data supply chain.
  arXiv  Detail & Related papers  (2024-12-05T02:37:51Z)
• A Survey on Data Markets [73.07800441775814]
  Growing trend of trading data for greater welfare has led to the emergence of data markets. A data market is any mechanism whereby the exchange of data products including datasets and data derivatives takes place. It serves as a coordinating mechanism by which several functions, including the pricing and the distribution of data, interact.
  arXiv  Detail & Related papers  (2024-11-09T15:09:24Z)
• Private, Augmentation-Robust and Task-Agnostic Data Valuation Approach for Data Marketplace [56.78396861508909]
  PriArTa is an approach for computing the distance between the distribution of the buyer's existing dataset and the seller's dataset. PriArTa is communication-efficient, enabling the buyer to evaluate datasets without needing access to the entire dataset from each seller.
  arXiv  Detail & Related papers  (2024-11-01T17:13:14Z)
• The Future of International Data Transfers: Managing Legal Risk with a User-Held Data Model [0.0]
  The General Data Protection Regulation contains a blanket prohibition on the transfer of personal data outside of the European Economic Area unless strict requirements are met. New technologies have made international data transfers the norm and not the exception. This article examines one such alternative, namely a user-held data model.
  arXiv  Detail & Related papers  (2024-07-30T03:15:36Z)
• Data Acquisition: A New Frontier in Data-centric AI [65.90972015426274]
  We first present an investigation of current data marketplaces, revealing lack of platforms offering detailed information about datasets. We then introduce the DAM challenge, a benchmark to model the interaction between the data providers and acquirers. Our evaluation of the submitted strategies underlines the need for effective data acquisition strategies in Machine Learning.
  arXiv  Detail & Related papers  (2023-11-22T22:15:17Z)
• More Data Types More Problems: A Temporal Analysis of Complexity, Stability, and Sensitivity in Privacy Policies [0.0]
  Data brokers and data processors are part of a multi-billion-dollar industry that profits from collecting, buying, and selling consumer data. Yet there is little transparency in the data collection industry which makes it difficult to understand what types of data are being collected, used, and sold.
  arXiv  Detail & Related papers  (2023-02-17T15:21:24Z)
• Lessons from the AdKDD'21 Privacy-Preserving ML Challenge [57.365745458033075]
  A prominent proposal at W3C only allows sharing advertising signals through aggregated, differentially private reports of past displays. To study this proposal extensively, an open Privacy-Preserving Machine Learning Challenge took place at AdKDD'21. A key finding is that learning models on large, aggregated data in the presence of a small set of unaggregated data points can be surprisingly efficient and cheap.
  arXiv  Detail & Related papers  (2022-01-31T11:09:59Z)
• Data Sharing Markets [95.13209326119153]
  We study a setup where each agent can be both buyer and seller of data. We consider two cases: bilateral data exchange (trading data with data) and unilateral data exchange (trading data with money)
  arXiv  Detail & Related papers  (2021-07-19T06:00:34Z)
• OSOUM Framework for Trading Data Research [79.0383470835073]
  We supply, to the best of our knowledge, the first open source simulation platform, Open SOUrce Market Simulator (OSOUM) to analyze trading markets and specifically data markets. We describe and implement a specific data market model, consisting of two types of agents: sellers who own various datasets available for acquisition, and buyers searching for relevant and beneficial datasets for purchase. Although commercial frameworks, intended for handling data markets, already exist, we provide a free and extensive end-to-end research tool for simulating possible behavior for both buyers and sellers participating in (data) markets.
  arXiv  Detail & Related papers  (2021-02-18T09:20:26Z)
• Data Requests and Scenarios for Data Design of Unobserved Events in Corona-related Confusion Using TEEDA [0.11470070927586014]
  In this study, we use the interactive platform called treasuring every encounter of data affairs (TEEDA) to externalize data requests from data users. We analyze the characteristics of missing data in the corona-related confusion stemming from both the data requests and the providable data obtained in the workshop.
  arXiv  Detail & Related papers  (2020-09-08T23:40:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.