The Privacy-Utility Trade-off in the Topics API

• URL: http://arxiv.org/abs/2406.15309v1
• Date: Fri, 21 Jun 2024 17:01:23 GMT
• Title: The Privacy-Utility Trade-off in the Topics API
• Authors: Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Gabriel H. Nunes,
• Abstract summary: We analyze the re-identification risks for individual Internet users and the utility provided to advertising companies by the Topics API. We provide theoretical results dependent only on the API parameters that can be readily applied to evaluate the privacy and utility implications of future API updates.
• Score: 0.34952465649465553
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The ongoing deprecation of third-party cookies by web browser vendors has sparked the proposal of alternative methods to support more privacy-preserving personalized advertising on web browsers and applications. The Topics API is being proposed by Google to provide third-parties with "coarse-grained advertising topics that the page visitor might currently be interested in". In this paper, we analyze the re-identification risks for individual Internet users and the utility provided to advertising companies by the Topics API, i.e. learning the most popular topics and distinguishing between real and random topics. We provide theoretical results dependent only on the API parameters that can be readily applied to evaluate the privacy and utility implications of future API updates, including novel general upper-bounds that account for adversaries with access to unknown, arbitrary side information, the value of the differential privacy parameter $\epsilon$, and experimental results on real-world data that validate our theoretical model.

Related papers

• Evaluating Google's Protected Audience Protocol [7.737740676767729]
  Google has proposed the Privacy Sandbox initiative to enable ad targeting without third-party cookies. This work focuses on analyzing linkage privacy risks for the reporting mechanisms proposed in the Protected Audience proposal.
  arXiv  Detail & Related papers  (2024-05-13T18:28:56Z)
• A Public and Reproducible Assessment of the Topics API on Real Data [1.1510009152620668]
  The Topics API for the web is Google's privacy-enhancing alternative to replace third-party cookies. Results of prior work have led to an ongoing discussion about the capability of Topics to trade off both utility and privacy. This paper shows on real data that Topics does not provide the same privacy guarantees to all users and that the information leakage worsens over time.
  arXiv  Detail & Related papers  (2024-03-28T17:03:44Z)
• Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API [51.00674811394867]
  The Attribution Reporting API has been deployed by Google Chrome to support the basic advertising functionality of attribution reporting. We present methods for optimizing the allocation of the contribution budget for summary reports from the API.
  arXiv  Detail & Related papers  (2023-11-22T18:45:20Z)
• PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners [81.571305826793]
  We introduce Contextual Privacy Protection Language Models (PrivacyMind) Our work offers a theoretical analysis for model design and benchmarks various techniques. In particular, instruction tuning with both positive and negative examples stands out as a promising method.
  arXiv  Detail & Related papers  (2023-10-03T22:37:01Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• A Quantitative Information Flow Analysis of the Topics API [0.34952465649465553]
  We analyze the re-identification risk for individual Internet users introduced by the Topics API from the perspective of information- and decision-theoretic framework. Our model allows a theoretical analysis of both privacy and utility aspects of the API and their trade-off, and we show that the Topics API does have better privacy than third-party cookies.
  arXiv  Detail & Related papers  (2023-09-26T08:14:37Z)
• On the Robustness of Topics API to a Re-Identification Attack [6.157783777246449]
  Google proposed the Topics API framework as a privacy-friendly alternative for behavioural advertising. This paper evaluates the robustness of the Topics API to a re-identification attack. We find that the Topics API mitigates but cannot prevent re-identification to take place, as there is a sizeable chance that a user's profile is unique within a website's audience.
  arXiv  Detail & Related papers  (2023-06-08T10:53:48Z)
• 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)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• 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)
• Is Downloading this App Consistent with my Values? Conceptualizing a Value-Centered Privacy Assistant [0.0]
  I propose that data privacy decisions can be understood as an expression of user values. I further propose the creation of a value-centered privacy assistant (VcPA)
  arXiv  Detail & Related papers  (2021-06-23T15:08:58Z)

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.