What's Privacy Good for? Measuring Privacy as a Shield from Harms due to Personal Data Use

• URL: http://arxiv.org/abs/2506.22787v1
• Date: Sat, 28 Jun 2025 07:00:37 GMT
• Title: What's Privacy Good for? Measuring Privacy as a Shield from Harms due to Personal Data Use
• Authors: Sri Harsha Gajavalli, Junichi Koizumi, Rakibul Hasan,
• Abstract summary: We operationalize this conceptualization in an online study with 400 college and university students.<n>Study participants indicated their perceptions of different harms that may arise when artificial intelligence-based algorithms infer personal data.<n> Comprehensive statistical analyses of the study data show that the 14 harms are internally consistent and collectively represent a general notion of privacy harms.
• Score: 6.662199530439544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a harm-centric conceptualization of privacy that asks: What harms from personal data use can privacy prevent? The motivation behind this research is limitations in existing privacy frameworks (e.g., Contextual Integrity) to capture or categorize many of the harms that arise from modern technology's use of personal data. We operationalize this conceptualization in an online study with 400 college and university students. Study participants indicated their perceptions of different harms (e.g., manipulation, discrimination, and harassment) that may arise when artificial intelligence-based algorithms infer personal data (e.g., demographics, personality traits, and cognitive disability) and use it to identify students who are likely to drop out of a course or the best job candidate. The study includes 14 harms and six types of personal data selected based on an extensive literature review. Comprehensive statistical analyses of the study data show that the 14 harms are internally consistent and collectively represent a general notion of privacy harms. The study data also surfaces nuanced perceptions of harms, both across the contexts and participants' demographic factors. Based on these results, we discuss how privacy can be improved equitably. Thus, this research not only contributes to enhancing the understanding of privacy as a concept but also provides practical guidance to improve privacy in the context of education and employment.

Related papers

• Enforcing Demographic Coherence: A Harms Aware Framework for Reasoning about Private Data Release [14.939460540040459]
  We introduce demographic coherence, a condition inspired by privacy attacks that we argue is necessary for data privacy.<n>Our framework focuses on confidence rated predictors, which can in turn be distilled from almost any data-informed process.<n>We prove that every differentially private data release is also demographically coherent, and that there are demographically coherent algorithms which are not differentially private.
  arXiv  Detail & Related papers  (2025-02-04T20:42:30Z)
• Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy [64.32494202656801]
  Privacy-preserving computer vision is an important emerging problem in machine learning and artificial intelligence.<n>We present anonymization pipeline that replaces sensitive human subjects in video datasets with synthetic avatars within context.<n>We also proposeMaskDP to protect non-anonymized but privacy sensitive background information.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory [43.12744258781724]
  We formulate the privacy issue as a reasoning problem rather than simple pattern matching.<n>We develop the first comprehensive checklist that covers social identities, private attributes, and existing privacy regulations.
  arXiv  Detail & Related papers  (2024-08-19T14:48:04Z)
• Embedding Privacy in Computational Social Science and Artificial Intelligence Research [2.048226951354646]
  Preserving privacy has emerged as a critical factor in research. The increasing use of advanced computational models stands to exacerbate privacy concerns. This article contributes to the field by discussing the role of privacy and the issues that researchers working in CSS, AI, data science and related domains are likely to face.
  arXiv  Detail & Related papers  (2024-04-17T16:07:53Z)
• 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)
• Systematic Review on Privacy Categorization [1.5377372227901214]
  This work aims to present a systematic review of the literature on privacy categorization. Privacy categorization involves the possibility to classify users according to specific prerequisites.
  arXiv  Detail & Related papers  (2023-07-07T15:18:26Z)
• Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining [75.25943383604266]
  We question whether the use of large Web-scraped datasets should be viewed as differential-privacy-preserving. We caution that publicizing these models pretrained on Web data as "private" could lead to harm and erode the public's trust in differential privacy as a meaningful definition of privacy. We conclude by discussing potential paths forward for the field of private learning, as public pretraining becomes more popular and powerful.
  arXiv  Detail & Related papers  (2022-12-13T10:41:12Z)
• 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)
• The Privacy Onion Effect: Memorization is Relative [76.46529413546725]
  We show an Onion Effect of memorization: removing the "layer" of outlier points that are most vulnerable exposes a new layer of previously-safe points to the same attack. It suggests that privacy-enhancing technologies such as machine unlearning could actually harm the privacy of other users.
  arXiv  Detail & Related papers  (2022-06-21T15:25:56Z)
• User Perception of Privacy with Ubiquitous Devices [5.33024001730262]
  This study aims to explore and discover various concerns related to perception of privacy in this era of ubiquitous technologies. Key themes like attitude towards privacy in public and private spaces, privacy awareness, consent seeking, dilemmas/confusions related to various technologies, impact of attitude and beliefs on individuals actions regarding how to protect oneself from invasion of privacy in both public and private spaces.
  arXiv  Detail & Related papers  (2021-07-23T05:01:44Z)
• Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach [54.32266555843765]
  This paper studies a model that protects the privacy of the individuals sensitive information while also allowing it to learn non-discriminatory predictors. The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints.
  arXiv  Detail & Related papers  (2020-09-26T10:50:33Z)

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.