SoK: Usability Studies in Differential Privacy

• URL: http://arxiv.org/abs/2412.16825v1
• Date: Sun, 22 Dec 2024 02:21:57 GMT
• Title: SoK: Usability Studies in Differential Privacy
• Authors: Onyinye Dibia, Brad Stenger, Steven Baldasty, Mako Bates, Ivoline C. Ngong, Yuanyuan Feng, Joseph P. Near,
• Abstract summary: Differential Privacy (DP) has emerged as a pivotal approach for safeguarding individual privacy in data analysis. This paper presents a comprehensive systematization of existing research on the usability of and communication about DP.
• Score: 3.4111656179349743
• License:
• Abstract: Differential Privacy (DP) has emerged as a pivotal approach for safeguarding individual privacy in data analysis, yet its practical adoption is often hindered by challenges in usability in implementation and communication of the privacy protection levels. This paper presents a comprehensive systematization of existing research on the usability of and communication about DP, synthesizing insights from studies on both the practical use of DP tools and strategies for conveying DP parameters that determine the privacy protection levels such as epsilon. By reviewing and analyzing these studies, we identify core usability challenges, best practices, and critical gaps in current DP tools that affect adoption across diverse user groups, including developers, data analysts, and non-technical stakeholders. Our analysis highlights actionable insights and pathways for future research that emphasizes user-centered design and clear communication, fostering the development of more accessible DP tools that meet practical needs and support broader adoption.

Related papers

• A Decade of Metric Differential Privacy: Advancements and Applications [8.865292595200964]
  Metric Differential Privacy (mDP) builds upon the core principles of Differential Privacy (DP) by incorporating various distance metrics. mDP offers privacy guarantees for a wide range of applications, such as location-based services, text analysis, and image processing. This paper provides a comprehensive survey of mDP research from 2013 to 2024, tracing its development from the foundations of DP.
  arXiv  Detail & Related papers  (2025-02-13T05:18:24Z)
• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder. Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• Advancing privacy in learning analytics using differential privacy [0.412484724941528]
  This paper addresses the challenge of balancing learner data privacy with the use of data in learning analytics (LA) by proposing a novel framework by applying Differential Privacy (DP) We introduce the first DP framework specifically designed for LA and provide practical guidance for its implementation. We demonstrate the use of this framework through a LA usage scenario and validate DP in safeguarding data privacy against potential attacks through an experiment on a well-known LA dataset.
  arXiv  Detail & Related papers  (2025-01-03T12:36:11Z)
• But Can You Use It? Design Recommendations for Differentially Private Interactive Systems [0.499320937849508]
  This work outlines and considers the barriers to developing differentially private interactive systems for informing public policy. We propose balancing three design considerations: privacy assurance, statistical utility, and system usability. Our work seeks to move the practical development of differentially private interactive systems forward to better aid public policy making and spark future research.
  arXiv  Detail & Related papers  (2024-12-16T14:07:16Z)
• Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
  Security measures such as anonymization are needed to protect individuals' privacy. Within our study, we conducted expert interviews to gain insights into practices in the field. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
  arXiv  Detail & Related papers  (2024-07-04T08:29:27Z)
• 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)
• Centering Policy and Practice: Research Gaps around Usable Differential Privacy [12.340264479496375]
  We argue that while differential privacy is a clean formulation in theory, it poses significant challenges in practice. To bridge the gaps between differential privacy's promises and its real-world usability, researchers and practitioners must work together.
  arXiv  Detail & Related papers  (2024-06-17T21:32:30Z)
• 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)
• Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment [100.1798289103163]
  We present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP) Key points and high-level contents of the article were originated from the discussions from "Differential Privacy (DP): Challenges Towards the Next Frontier" This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions.
  arXiv  Detail & Related papers  (2023-04-14T05:29:18Z)
• Semantics-Preserved Distortion for Personal Privacy Protection in Information Management [65.08939490413037]
  This paper suggests a linguistically-grounded approach to distort texts while maintaining semantic integrity. We present two distinct frameworks for semantic-preserving distortion: a generative approach and a substitutive approach. We also explore privacy protection in a specific medical information management scenario, showing our method effectively limits sensitive data memorization.
  arXiv  Detail & Related papers  (2022-01-04T04:01:05Z)

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.