The Importance of Collective Privacy in Digital Sexual and Reproductive Health

• URL: http://arxiv.org/abs/2311.15432v1
• Date: Sun, 26 Nov 2023 21:25:08 GMT
• Title: The Importance of Collective Privacy in Digital Sexual and Reproductive Health
• Authors: Teresa Almeida, Maryam Mehrnezhad, Stephen Cook
• Abstract summary: We analyzed 15 Internet of Things devices with sexual and reproductive tracking services. Results suggest that digital sexual and reproductive health data privacy is both an individual and collective endeavor.
• Score: 5.524804393257921
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is an abundance of digital sexual and reproductive health technologies that presents a concern regarding their potential sensitive data breaches. We analyzed 15 Internet of Things (IoT) devices with sexual and reproductive tracking services and found this ever-extending collection of data implicates many beyond the individual including partner, child, and family. Results suggest that digital sexual and reproductive health data privacy is both an individual and collective endeavor.

Related papers

• Safer Digital Intimacy For Sex Workers And Beyond: A Technical Research Agenda [21.70034795348216]
  Many people engage in digital intimacy: sex workers, their clients, and people who create and share intimate content recreationally. With this intimacy comes significant security and privacy risk, exacerbated by stigma. In this article, we present a commercial digital intimacy threat model and 10 research directions for safer digital intimacy.
  arXiv  Detail & Related papers  (2024-03-15T21:16:01Z)
• Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
  The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare. The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body. HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed. Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
  arXiv  Detail & Related papers  (2024-01-22T03:17:41Z)
• Preserving The Safety And Confidentiality Of Data Mining Information In Health Care: A literature review [0.0]
  PPDM technique enables the extraction of actionable insight from enormous volume of data. Disclosing sensitive information infringes on patients' privacy. This paper aims to conduct a review of related work on privacy-preserving mechanisms, data protection regulations, and mitigating tactics.
  arXiv  Detail & Related papers  (2023-10-30T05:32:15Z)
• 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)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems [0.0]
  Data transferred to the digital environment pose a threat of privacy leakage. In this study, it is aimed to propose a model to handle the privacy problems based on federated learning. Our proposed model presents an extensive privacy and data analysis and achieve high performance.
  arXiv  Detail & Related papers  (2021-09-29T10:39:25Z)
• A Systematic Literature Review on Wearable Health Data Publishing under Differential Privacy [2.099922236065961]
  Wearable devices generate different types of physiological data about the individuals. Differential Privacy (DP) has emerged as a proficient technique to publish privacy sensitive data.
  arXiv  Detail & Related papers  (2021-09-15T14:43:00Z)
• COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics [116.6248556979572]
  COVIDx-US is an open-access benchmark dataset of COVID-19 related ultrasound imaging data. It consists of 93 lung ultrasound videos and 10,774 processed images of patients infected with SARS-CoV-2 pneumonia, non-SARS-CoV-2 pneumonia, as well as healthy control cases.
  arXiv  Detail & Related papers  (2021-03-18T03:31:33Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)
• Secondary Use of Electronic Health Record: Opportunities and Challenges [0.0]
  Using EHR data for secondary purposes without consent creates privacy issues for individuals. Sharing of EHR across multiples agencies makes it vulnerable to cyber attacks. Data leak can cause financial losses or an individuals may encounter social boycott if their medical condition is exposed in public.
  arXiv  Detail & Related papers  (2020-01-26T16:22:53Z)

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.