Privacy Without Remedy: An Assessment of Data Broker Compliance with California Privacy Law

• URL: http://arxiv.org/abs/2605.21376v1
• Date: Wed, 20 May 2026 16:39:19 GMT
• Title: Privacy Without Remedy: An Assessment of Data Broker Compliance with California Privacy Law
• Abstract summary: 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.
• Score: 4.524476021202175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: California's consumer privacy law is widely deemed to be the most protected in the United States, one of the few to expressly regulate third party entities that buy and sell consumer data (data brokers). We offer the first empirical assessment of data broker compliance with the 2018 California Consumer Privacy Act (CCPA) and the 2023 Delete Act, which requires data brokers to register with the state and report consumer rights requests metrics annually. First, we demonstrate that only 9% of 522 registered data brokers were fully compliant with transparency requirements after the Delete Act took effect, although we do identify slight improvements over time. Second, we descriptively characterize wide heterogeneity across data brokers in the volume of consumer rights requests received, with many reporting none. We bring in external business data to explore correlates associated with this variation, a challenge given the general lack of opacity into broker business practices. Third, in an audit of a sample of 250 data brokers' consumers request processes, we find that 43% make it impossible for consumers to exercise all privacy rights and 64% introduce at least one design feature that creates substantial friction into the consumer request process. Last, we show how these deficiencies stem from the decentralization of compliance decisions to brokers themselves, enforcement limitations, and regulatory ambiguity. We articulate reforms that could improve consumer privacy, transparency in broker practices, and compliance with these laws.

Related papers

• Let My Data Go: Data Brokers' Compliance with Opt-Out and Deletion Requests [9.601311112679367]
  Data brokers are a largely American phenomenon.<n>California Consumer Privacy Act (CCPA) modeled after EU's General Data Protection Regulation.<n>Some data brokers require intrusive consumer identity verification in order to exercise one's opt-out rights.
  arXiv  Detail & Related papers  (2026-07-05T23:52:18Z)
• The Perfect Match? A Closer Look at the Relationship between EU Consumer Law and Data Protection Law [0.8076386449963743]
  In modern markets, many companies offer so-called 'free' services and monetize consumer data they collect through those services.<n>This paper argues that consumer law and data protection law can usefully complement each other.
  arXiv  Detail & Related papers  (2025-10-15T12:13:35Z)
• Consumer Beware! Exploring Data Brokers' CCPA Compliance [8.562189001930745]
  The California Consumer Privacy Act (CCPA) grants consumers the legal right to request access to, or deletion of, their data.<n>To facilitate these requests, California maintains an official registry of data brokers.<n>This paper presents the first large-scale, systematic study of CCPA compliance of all 543 officially registered data brokers.
  arXiv  Detail & Related papers  (2025-06-27T04:57:32Z)
• Privacy's Peril: Unmasking the Unregulated Underground Market of Data Brokers and the Suggested Framework [0.0]
  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.
  arXiv  Detail & Related papers  (2024-10-06T19:51:31Z)
• Language Models Can Reduce Asymmetry in Information Markets [100.38786498942702]
  We introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information.
  arXiv  Detail & Related papers  (2024-03-21T14:48:37Z)
• Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
  We design an intelligent approach to online privacy protection that leverages supervised learning. By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
  arXiv  Detail & Related papers  (2023-04-06T05:20:16Z)
• Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest [70.02478301291264]
  Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse. Prior studies have used black-box methods to show that these algorithms can lead to biased or discriminatory outcomes. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation.
  arXiv  Detail & Related papers  (2022-07-18T17:32:35Z)
• Fighting the Fog: Evaluating the Clarity of Privacy Disclosures in the Age of CCPA [29.56312492076473]
  Vagueness and ambiguity in privacy policies threaten the ability of consumers to make informed choices about how businesses collect, use, and share personal information. The California Consumer Privacy Act (CCPA) of 2018 was intended to provide Californian consumers with more control by mandating that businesses clearly disclose their data practices. Our results suggest that CCPA's mandates for privacy disclosures, as currently implemented, have not yet yielded the level of clarity they were designed to deliver.
  arXiv  Detail & Related papers  (2021-09-28T15:40:57Z)
• Preference-Based Privacy Trading [19.23266277956912]
  We propose a design of regulated efficient/bounded inefficient economic mechanisms for oligopoly data trading markets using a novel preference function bidding approach on a simplified sellers-broker market. Our methodology preserves the heterogeneous privacy preservation constraints (at a grouped consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfies information demand (via the broker) of agencies.
  arXiv  Detail & Related papers  (2020-12-10T07:03:10Z)
• Second layer data governance for permissioned blockchains: the privacy management challenge [58.720142291102135]
  In pandemic situations, such as the COVID-19 and Ebola outbreak, the action related to sharing health data is crucial to avoid the massive infection and decrease the number of deaths. In this sense, permissioned blockchain technology emerges to empower users to get their rights providing data ownership, transparency, and security through an immutable, unified, and distributed database ruled by smart contracts.
  arXiv  Detail & Related papers  (2020-10-22T13:19:38Z)
• A vision for global privacy bridges: Technical and legal measures for international data markets [77.34726150561087]
  Despite data protection laws and an acknowledged right to privacy, trading personal information has become a business equated with "trading oil" An open conflict is arising between business demands for data and a desire for privacy. We propose and test a vision of a personal information market with privacy.
  arXiv  Detail & Related papers  (2020-05-13T13:55:50Z)
• Beyond privacy regulations: an ethical approach to data usage in transportation [64.86110095869176]
  We describe how Federated Machine Learning can be applied to the transportation sector. We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy.
  arXiv  Detail & Related papers  (2020-04-01T15:10:12Z)

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.