Witnessing the blooming adoption of push notifications
on mobile devices, this new message delivery paradigm has
become pervasive in diverse applications. Accompanying with its
broad adoption, the potential security risks and privacy exposure
issues raise public concerns regarding its great social impacts.
This paper conducts the first attempt to exploit the mobile
notification ecosystem. By dissecting its structural elements and
implementation process, a comprehensive vulnerability analysis
is conducted towards the complete flow of mobile notification
from platform enrollment to messaging. Meanwhile, for privacy
exposure, we first examine the implementation of privacy policy
compliance by proposing a three-level inspection approach to
guide our analysis. Then, our top-down methods from documentation
analysis, application network traffic study, to static analysis
expose the illicit data collection behaviors in released applications.
In addition, we uncover the potential privacy inference resulted
from the notification monitoring. To support our analysis, we
conduct empirical studies on 12 most popular notification platforms
and perform static analysis over 30,000+ applications.
We discover: 1) six platforms either provide ambiguous KEY
naming rules or offer vulnerable messaging APIs; 2) privacy
policy compliance implementations are either stagnated at the
documentation stages (8 of 12 platforms) or never implemented
in apps, resulting in billions of users suffering from privacy
exposure; and 3) some apps can stealthily monitor notification
messages delivering to other apps, potentially incurring user
privacy inference risks. Our study raises the urgent demand for
better regulations of mobile notification deployment.
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Epidemic management and control through risk-dependent individual contact interventions
Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.
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- Award ID(s):
- 1835576
- NSF-PAR ID:
- 10381663
- Editor(s):
- Moreno, Yamir
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 18
- Issue:
- 6
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1010171
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pcbi.1010171
