Towards a decentralized data privacy protocol for self-sovereignty in the digital world

• URL: http://arxiv.org/abs/2404.12837v1
• Date: Fri, 19 Apr 2024 12:19:04 GMT
• Title: Towards a decentralized data privacy protocol for self-sovereignty in the digital world
• Authors: Rodrigo Falcão, Arghavan Hosseinzadeh,
• Abstract summary: We propose a paradigm shift towards an enriched user-centric approach for cross-service privacy preferences management. In this vision paper, we propose the realization of a decentralized data privacy protocol.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A typical user interacts with many digital services nowadays, providing these services with their data. As of now, the management of privacy preferences is service-centric: Users must manage their privacy preferences according to the rules of each service provider, meaning that every provider offers its unique mechanisms for users to control their privacy settings. However, managing privacy preferences holistically (i.e., across multiple digital services) is just impractical. In this vision paper, we propose a paradigm shift towards an enriched user-centric approach for cross-service privacy preferences management: the realization of a decentralized data privacy protocol.

Related papers

• Masked Differential Privacy [64.32494202656801]
  We propose an effective approach called masked differential privacy (DP), which allows for controlling sensitive regions where differential privacy is applied. Our method operates selectively on data and allows for defining non-sensitive-temporal regions without DP application or combining differential privacy with other privacy techniques within data samples.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• Privacy-Preserving Data Management using Blockchains [0.0]
  Data providers need to control and update existing privacy preferences due to changing data usage. This paper proposes a blockchain-based methodology for preserving data providers private and sensitive data.
  arXiv  Detail & Related papers  (2024-08-21T01:10:39Z)
• Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning [62.224804688233]
  differential privacy (DP) offers a promising solution by ensuring models are 'almost indistinguishable' with or without any particular privacy unit. We study user-level DP motivated by applications where it necessary to ensure uniform privacy protection across users.
  arXiv  Detail & Related papers  (2024-06-20T13:54:32Z)
• Randomized algorithms for precise measurement of differentially-private, personalized recommendations [6.793345945003182]
  We propose an algorithm for personalized recommendations that facilitates both precise and differentially-private measurement. We conduct offline experiments to quantify how the proposed privacy-preserving algorithm affects key metrics related to user experience, advertiser value, and platform revenue.
  arXiv  Detail & Related papers  (2023-08-07T17:34:58Z)
• 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)
• 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)
• Algorithms with More Granular Differential Privacy Guarantees [65.3684804101664]
  We consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis. In this work, we study several basic data analysis and learning tasks, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person.
  arXiv  Detail & Related papers  (2022-09-08T22:43:50Z)
• Leveraging Privacy Profiles to Empower Users in the Digital Society [7.350403786094707]
  Privacy and ethics of citizens are at the core of the concerns raised by our increasingly digital society. We focus on the privacy dimension and contribute a step in the above direction through an empirical study on an existing dataset collected from the fitness domain. The results reveal that a compact set of semantic-driven questions helps distinguish users better than a complex domain-dependent one.
  arXiv  Detail & Related papers  (2022-04-01T15:31:50Z)
• Ride Sharing & Data Privacy: An Analysis of the State of Practice [0.0]
  We analyzed how popular ride sharing services handle user privacy to assess the current state of practice. The results show that services include a varying set of personal data and offer limited privacy-related features.
  arXiv  Detail & Related papers  (2021-10-18T11:06:06Z)
• PrivEdge: From Local to Distributed Private Training and Prediction [43.02041269239928]
  PrivEdge is a technique for privacy-preserving Machine Learning (ML) PrivEdge safeguards the privacy of users who provide their data for training, as well as users who use the prediction service. We show that PrivEdge has high precision and recall in preserving privacy, as well as in distinguishing between private and non-private images.
  arXiv  Detail & Related papers  (2020-04-12T09:26:12Z)

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.