Toward the Cure of Privacy Policy Reading Phobia: Automated Generation of Privacy Nutrition Labels From Privacy Policies

• URL: http://arxiv.org/abs/2306.10923v1
• Date: Mon, 19 Jun 2023 13:33:44 GMT
• Title: Toward the Cure of Privacy Policy Reading Phobia: Automated Generation of Privacy Nutrition Labels From Privacy Policies
• Authors: Shidong Pan, Thong Hoang, Dawen Zhang, Zhenchang Xing, Xiwei Xu, Qinghua Lu, and Mark Staples
• Abstract summary: We propose the first framework that can automatically generate privacy nutrition labels from privacy policies. Based on our ground truth applications about the Data Safety Report from the Google Play app store, our framework achieves a 0.75 F1-score on generating first-party data collection practices. We also analyse the inconsistencies between ground truth and curated privacy nutrition labels on the market, and our framework can detect 90.1% under-claim issues.
• Score: 19.180437130066323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software applications have become an omnipresent part of modern society. The consequent privacy policies of these applications play a significant role in informing customers how their personal information is collected, stored, and used. However, customers rarely read and often fail to understand privacy policies because of the ``Privacy Policy Reading Phobia'' (PPRP). To tackle this emerging challenge, we propose the first framework that can automatically generate privacy nutrition labels from privacy policies. Based on our ground truth applications about the Data Safety Report from the Google Play app store, our framework achieves a 0.75 F1-score on generating first-party data collection practices and an average of 0.93 F1-score on general security practices. We also analyse the inconsistencies between ground truth and curated privacy nutrition labels on the market, and our framework can detect 90.1% under-claim issues. Our framework demonstrates decent generalizability across different privacy nutrition label formats, such as Google's Data Safety Report and Apple's App Privacy Details.

Related papers

• Interactive GDPR-Compliant Privacy Policy Generation for Software Applications [6.189770781546807]
  To use software applications users are sometimes requested to provide their personal information. As privacy has become a significant concern many protection regulations exist worldwide. We propose an approach that generates comprehensive and compliant privacy policy.
  arXiv  Detail & Related papers  (2024-10-04T01:22:16Z)
• PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action [54.11479432110771]
  PrivacyLens is a novel framework designed to extend privacy-sensitive seeds into expressive vignettes and further into agent trajectories. We instantiate PrivacyLens with a collection of privacy norms grounded in privacy literature and crowdsourced seeds. State-of-the-art LMs, like GPT-4 and Llama-3-70B, leak sensitive information in 25.68% and 38.69% of cases, even when prompted with privacy-enhancing instructions.
  arXiv  Detail & Related papers  (2024-08-29T17:58:38Z)
• {A New Hope}: Contextual Privacy Policies for Mobile Applications and An Approach Toward Automated Generation [19.578130824867596]
  The aim of contextual privacy policies ( CPPs) is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application's graphical user interfaces (GUIs) In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SeePrivacy, specifically designed to automatically generate CPPs for mobile applications. A human evaluation shows that 77% of the extracted privacy policy segments were perceived as well-aligned with the detected contexts.
  arXiv  Detail & Related papers  (2024-02-22T13:32:33Z)
• SeePrivacy: Automated Contextual Privacy Policy Generation for Mobile Applications [21.186902172367173]
  SeePrivacy is designed to automatically generate contextual privacy policies for mobile apps. Our method synergistically combines mobile GUI understanding and privacy policy document analysis. 96% of the retrieved policy segments can be correctly matched with their contexts.
  arXiv  Detail & Related papers  (2023-07-04T12:52:45Z)
• Is It a Trap? A Large-scale Empirical Study And Comprehensive Assessment of Online Automated Privacy Policy Generators for Mobile Apps [15.181098379077344]
  Automated Privacy Policy Generators can create privacy policies for mobile apps. Nearly 20.1% of privacy policies could be generated by existing APPGs. App developers must carefully select and use the appropriate APPGs to avoid potential pitfalls.
  arXiv  Detail & Related papers  (2023-05-05T04:08:18Z)
• The Overview of Privacy Labels and their Compatibility with Privacy Policies [24.871967983289117]
  Privacy nutrition labels provide a way to understand an app's key data practices without reading the long and hard-to-read privacy policies. Apple and Google have implemented mandates requiring app developers to fill privacy nutrition labels highlighting their privacy practices.
  arXiv  Detail & Related papers  (2023-03-14T20:10:28Z)
• Privacy Explanations - A Means to End-User Trust [64.7066037969487]
  We looked into how explainability might help to tackle this problem. We created privacy explanations that aim to help to clarify to end users why and for what purposes specific data is required. Our findings reveal that privacy explanations can be an important step towards increasing trust in software systems.
  arXiv  Detail & Related papers  (2022-10-18T09:30:37Z)
• 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)
• Analysis of Longitudinal Changes in Privacy Behavior of Android Applications [79.71330613821037]
  In this paper, we examine the trends in how Android apps have changed over time with respect to privacy. We examine the adoption of HTTPS, whether apps scan the device for other installed apps, the use of permissions for privacy-sensitive data, and the use of unique identifiers. We find that privacy-related behavior has improved with time as apps continue to receive updates, and that the third-party libraries used by apps are responsible for more issues with privacy.
  arXiv  Detail & Related papers  (2021-12-28T16:21:31Z)
• PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning [111.19576084222345]
  This paper proposes a framework of Privacy-preserving Credit risk modeling based on Adversarial Learning (PCAL) PCAL aims to mask the private information inside the original dataset, while maintaining the important utility information for the target prediction task performance. Results indicate that PCAL can learn an effective, privacy-free representation from user data, providing a solid foundation towards privacy-preserving machine learning for credit risk analysis.
  arXiv  Detail & Related papers  (2020-10-06T07:04:59Z)
• Private Reinforcement Learning with PAC and Regret Guarantees [69.4202374491817]
  We design privacy preserving exploration policies for episodic reinforcement learning (RL) We first provide a meaningful privacy formulation using the notion of joint differential privacy (JDP) We then develop a private optimism-based learning algorithm that simultaneously achieves strong PAC and regret bounds, and enjoys a JDP guarantee.
  arXiv  Detail & Related papers  (2020-09-18T20:18:35Z)

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.