Ctrl-Shift: How Privacy Sentiment Changed from 2019 to 2021

• URL: http://arxiv.org/abs/2110.09437v2
• Date: Tue, 15 Mar 2022 15:21:36 GMT
• Title: Ctrl-Shift: How Privacy Sentiment Changed from 2019 to 2021
• Authors: Angelica Goetzen, Samuel Dooley, Elissa M. Redmiles
• Abstract summary: We study the sentiments of people in the U.S. toward collection and use of data for government- and health-related purposes from 2019-2021. After the onset of COVID-19, we observe significant decreases in respondent acceptance of government data use. Following the 2020 U.S. national elections, we observe some of the first evidence that privacy sentiments may change based on the alignment between a user's politics and the political party in power.
• Score: 14.600192799641077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: People's privacy sentiments influence changes in legislation as well as technology design and use. While single-point-in-time investigations of privacy sentiment offer useful insight, study of people's privacy sentiments over time is also necessary to better understand and anticipate evolving privacy attitudes. In this work, we use repeated cross-sectional surveys (n=6,676) to model the sentiments of people in the U.S. toward collection and use of data for government- and health-related purposes from 2019-2021. After the onset of COVID-19, we observe significant decreases in respondent acceptance of government data use and significant increases in acceptance of health-related data uses. While differences in privacy attitudes between sociodemographic groups largely decreased over this time period, following the 2020 U.S. national elections, we observe some of the first evidence that privacy sentiments may change based on the alignment between a user's politics and the political party in power. Our results offer insight into how privacy attitudes may have been impacted by recent events and allow us to identify potential predictors of changes in privacy attitudes during times of geopolitical or national change.

Related papers

• 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 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 Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications [76.88662943995641]
  Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data. To address this issue, researchers have started to develop privacy-preserving GNNs. Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain.
  arXiv  Detail & Related papers  (2023-08-31T00:31:08Z)
• How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
  We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
  arXiv  Detail & Related papers  (2022-11-18T11:39:03Z)
• 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)
• Privacy Policies Across the Ages: Content and Readability of Privacy Policies 1996--2021 [1.5229257192293197]
  We analyze the 25-year history of privacy policies using methods from transparency research, machine learning, and natural language processing. We collect a large-scale longitudinal corpus of privacy policies from 1996 to 2021. Our results show that policies are getting longer and harder to read, especially after new regulations take effect.
  arXiv  Detail & Related papers  (2022-01-21T15:13:02Z)
• 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)
• The Evolving Path of "the Right to Be Left Alone" - When Privacy Meets Technology [0.0]
  This paper proposes a novel vision of the privacy ecosystem, introducing privacy dimensions, the related users' expectations, the privacy violations, and the changing factors. We believe that promising approaches to tackle the privacy challenges move in two directions: (i) identification of effective privacy metrics; and (ii) adoption of formal tools to design privacy-compliant applications.
  arXiv  Detail & Related papers  (2021-11-24T11:27:55Z)
• Equity and Privacy: More Than Just a Tradeoff [10.545898004301323]
  Recent work has shown that privacy preserving data publishing can introduce different levels of utility across different population groups. Will marginal populations see disproportionately less utility from privacy technology? If there is an inequity how can we address it?
  arXiv  Detail & Related papers  (2021-11-08T17:39:32Z)
• Digital Divide and Social Dilemma of Privacy Preservation [0.6261444979025642]
  "Digital privacy divide (DPD)" is introduced to describe the perceived gap in the privacy preservation of individuals based on the geopolitical location of different countries. We created an online questionnaire and collected answers from more than 700 respondents from four different countries. Individuals residing in Germany and Bangladesh share similar privacy concerns, while there is a significant similarity among individuals residing in the United States and India.
  arXiv  Detail & Related papers  (2021-10-06T11:43:46Z)
• Privacy Policies over Time: Curation and Analysis of a Million-Document Dataset [6.060757543617328]
  We develop a crawler that discovers, downloads, and extracts archived privacy policies from the Internet Archive's Wayback Machine. We curated a dataset of 1,071,488 English language privacy policies, spanning over two decades and over 130,000 distinct websites. Our data indicate that self-regulation for first-party websites has stagnated, while self-regulation for third parties has increased but is dominated by online advertising trade associations.
  arXiv  Detail & Related papers  (2020-08-20T19:00:37Z)

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.