Mobile Mental Health Apps: Alternative Intervention or Intrusion?
- URL: http://arxiv.org/abs/2206.10728v2
- Date: Sat, 9 Jul 2022 20:12:15 GMT
- Title: Mobile Mental Health Apps: Alternative Intervention or Intrusion?
- Authors: Shalini Saini, Dhiral Panjwani, and Nitesh Saxena
- Abstract summary: Mobile Mental Health (MMH) apps emerge as an effective alternative to assist with a broad range of psychological disorders.
The absence of a transparent privacy policy and lack of user awareness may pose a significant threat to undermining the applicability of such tools.
Our results indicate that apps' exploitable flaws, dangerous permissions, and insecure data handling pose a potential threat to the users' privacy and security.
- Score: 2.131521514043068
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Mental health is an extremely important subject, especially in these
unprecedented times of the COVID-19 pandemic. Ubiquitous mobile phones can
equip users to supplement psychiatric treatment and manage their mental health.
Mobile Mental Health (MMH) apps emerge as an effective alternative to assist
with a broad range of psychological disorders filling the much-needed
patient-provider accessibility gap. However, it also raises significant
concerns with sensitive information leakage.The absence of a transparent
privacy policy and lack of user awareness may pose a significant threat to
undermining the applicability of such tools. We conducted a multifold study of
- 1) Privacy Policies (Manually and with Polisis, an automated framework to
evaluate privacy policies); 2) App permissions; 3) Static Analysis for inherent
security issues; 4) Dynamic Analysis for threat surface and vulnerabilities
detection, and 5) Traffic Analysis.
Our results indicate that apps' exploitable flaws, dangerous permissions, and
insecure data handling pose a potential threat to the users' privacy and
security. The Dynamic analysis identified 145 vulnerabilities in 20 top-rated
MMH apps where attackers and malicious apps can access sensitive information.
45% of MMH apps use a unique identifier, Hardware Id, which can link a unique
id to a particular user and probe users' mental health. Traffic analysis shows
that sensitive mental health data can be leaked through insecure data
transmission. MMH apps need better scrutiny and regulation for more widespread
usage to meet the increasing need for mental health care without being
intrusive to the already vulnerable population.
Related papers
- Security Analysis of Top-Ranked mHealth Fitness Apps: An Empirical Study [0.32885740436059047]
We investigate the security vulnerabilities of ten top-ranked Android health and fitness apps, a set that accounts for 237 million downloads.
Our findings revealed many vulnerabilities, such as insecure coding, hardcoded sensitive information, over-privileged permissions, misconfiguration, and excessive communication with third-party domains.
arXiv Detail & Related papers (2024-09-27T08:11:45Z) - Privacy Aware Question-Answering System for Online Mental Health Risk
Assessment [0.45935798913942893]
Social media platforms have enabled individuals suffering from mental illnesses to share their lived experiences and find the online support necessary to cope.
We propose a Question-Answering (QA) approach to assess mental health risk using the Unified-QA model on two large mental health datasets.
Our results demonstrate the effectiveness of modeling risk assessment as a QA task, specifically for mental health use cases.
arXiv Detail & Related papers (2023-06-09T03:37:49Z) - Mental Illness Classification on Social Media Texts using Deep Learning
and Transfer Learning [55.653944436488786]
According to the World health organization (WHO), approximately 450 million people are affected.
Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD.
This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
arXiv Detail & Related papers (2022-07-03T11:33:52Z) - On the Privacy of Mental Health Apps: An Empirical Investigation and its
Implications for Apps Development [14.113922276394588]
This paper reports an empirical study aimed at systematically identifying and understanding data privacy incorporated in mental health apps.
We analyzed 27 top-ranked mental health apps from Google Play Store.
The findings reveal important data privacy issues such as unnecessary permissions, insecure cryptography implementations, and leaks of personal data and credentials in logs and web requests.
arXiv Detail & Related papers (2022-01-22T09:23:56Z) - Learning Language and Multimodal Privacy-Preserving Markers of Mood from
Mobile Data [74.60507696087966]
Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care.
One promising data source to help monitor human behavior is daily smartphone usage.
We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
arXiv Detail & Related papers (2021-06-24T17:46:03Z) - Multimodal Privacy-preserving Mood Prediction from Mobile Data: A
Preliminary Study [34.550824104906255]
Mental health conditions remain under-diagnosed even in countries with common access to advanced medical care.
One promising data source to help monitor human behavior is from daily smartphone usage.
We study behavioral markers or daily mood using a recent dataset of mobile behaviors from high-risk adolescent populations.
arXiv Detail & Related papers (2020-12-04T01:44:22Z) - Anxiety Detection Leveraging Mobile Passive Sensing [53.11661460916551]
Anxiety disorders are the most common class of psychiatric problems affecting both children and adults.
Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods.
eWellness is an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner.
arXiv Detail & Related papers (2020-08-09T20:22:52Z) - Decentralized Privacy-Preserving Proximity Tracing [50.27258414960402]
DP3T provides a technological foundation to help slow the spread of SARS-CoV-2.
System aims to minimise privacy and security risks for individuals and communities.
arXiv Detail & Related papers (2020-05-25T12:32:02Z) - 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) - Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918.
Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases.
We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
arXiv Detail & Related papers (2020-04-16T15:53:42Z)
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.