Evaluating Google's Protected Audience Protocol

• URL: http://arxiv.org/abs/2405.08102v2
• Date: Mon, 20 May 2024 17:01:43 GMT
• Title: Evaluating Google's Protected Audience Protocol
• Authors: Minjun Long, David Evans,
• Abstract summary: 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.
• Score: 7.737740676767729
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While third-party cookies have been a key component of the digital marketing ecosystem for years, they allow users to be tracked across web sites in ways that raise serious privacy concerns. Google has proposed the Privacy Sandbox initiative to enable ad targeting without third-party cookies. While there have been several studies focused on other aspects of this initiative, there has been little analysis to date as to how well the system achieves the intended goal of preventing request linking. This work focuses on analyzing linkage privacy risks for the reporting mechanisms proposed in the Protected Audience (PrAu) proposal (previously known as FLEDGE), which is intended to enable online remarketing without using third-party cookies. We summarize the overall workflow of PrAu and highlight potential privacy risks associated with its proposed design, focusing on scenarios in which adversaries attempt to link requests to different sites to the same user. We show how a realistic adversary would be still able to use the privacy-protected reporting mechanisms to link user requests and conduct mass surveillance, even with correct implementations of all the currently proposed privacy mechanisms.

Related papers

• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• The Privacy-Utility Trade-off in the Topics API [0.34952465649465553]
  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.
  arXiv  Detail & Related papers  (2024-06-21T17:01:23Z)
• Words Blending Boxes. Obfuscating Queries in Information Retrieval using Differential Privacy [7.831978389504435]
  When an Information Retrieval System (IRS) does not protect the privacy of its users, sensitive information may be disclosed through the queries sent to the system. Recent improvements, especially in NLP, have shown the potential of using Differential Privacy to obfuscate texts. We propose Word Blending Boxes, a novel differentially private mechanism for query obfuscation.
  arXiv  Detail & Related papers  (2024-05-15T12:51:36Z)
• Secure Aggregation is Not Private Against Membership Inference Attacks [66.59892736942953]
  We investigate the privacy implications of SecAgg in federated learning. We show that SecAgg offers weak privacy against membership inference attacks even in a single training round. Our findings underscore the imperative for additional privacy-enhancing mechanisms, such as noise injection.
  arXiv  Detail & Related papers  (2024-03-26T15:07:58Z)
• TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection [59.04634695294402]
  Video anomaly detection (VAD) without human monitoring is a complex computer vision task. Privacy leakage in VAD allows models to pick up and amplify unnecessary biases related to people's personal information. We propose TeD-SPAD, a privacy-aware video anomaly detection framework that destroys visual private information in a self-supervised manner.
  arXiv  Detail & Related papers  (2023-08-21T22:42:55Z)
• Tapping into Privacy: A Study of User Preferences and Concerns on Trigger-Action Platforms [0.0]
  The Internet of Things (IoT) devices are rapidly increasing in popularity, with more individuals using Internet-connected devices that continuously monitor their activities. This work explores privacy concerns and expectations of end-users related to Trigger-Action platforms (TAPs) in the context of the Internet of Things (IoT) TAPs allow users to customize their smart environments by creating rules that trigger actions based on specific events or conditions.
  arXiv  Detail & Related papers  (2023-08-11T14:25:01Z)
• Node Injection Link Stealing Attack [0.649970685896541]
  We present a stealthy and effective attack that exposes privacy vulnerabilities in Graph Neural Networks (GNNs) by inferring private links within graph-structured data. Our work highlights the privacy vulnerabilities inherent in GNNs, underscoring the importance of developing robust privacy-preserving mechanisms for their application.
  arXiv  Detail & Related papers  (2023-07-25T14:51:01Z)
• User-Centered Security in Natural Language Processing [0.7106986689736825]
  dissertation proposes a framework of user-centered security in Natural Language Processing (NLP) It focuses on two security domains within NLP with great public interest.
  arXiv  Detail & Related papers  (2023-01-10T22:34:19Z)
• 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)
• 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)
• SPAct: Self-supervised Privacy Preservation for Action Recognition [73.79886509500409]
  Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. Recent developments of self-supervised learning (SSL) have unleashed the untapped potential of the unlabeled data. We present a novel training framework which removes privacy information from input video in a self-supervised manner without requiring privacy labels.
  arXiv  Detail & Related papers  (2022-03-29T02:56:40Z)

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.