Mobile Device Usage and Data Protection in Amazon: A Technical Report

This technical report examines mobile device usage and security within Amazon, focusing on key aspects of data protection. It explores physical and logical protection mechanisms, crucial for safeguarding data and network access. The report analyzes the challenges of protecting heterogeneous information and securing data in motion, including strategies like restricting cloud sharing and identifying critical assets. It compares and contrasts data protection models, such as comprehensive, sectoral, and self-regulatory models, and argues for the suitability of a data-centric model for Amazon's environment. The report also emphasizes the importance of encryption technologies for protecting personal and customer data. Finally, the report concludes with a discussion of data security and the implementation of encryption, providing a detailed overview of mobile device security measures within a corporate context.

Running head: MOBILE DEVICE USAGE IN AMAZON
MOBILE DEVICE USAGE IN AMAZON
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Table of Contents
Physical Protection Mechanism...........................................................................................2
Logical Protection Mechanism............................................................................................2
Protecting heterogeneous information.................................................................................3
Protecting data in Motion....................................................................................................3
Data Protection Models.......................................................................................................4
Making use of device-centric or data centric model............................................................4
Encryption............................................................................................................................5
References............................................................................................................................6

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Physical Protection Mechanism
Physical security is all restricting access to the network by putting all the resource in
locked doors. This step will help in protecting from both the disaster that is natural and human-
made. This particular step will help in protecting the access to network from different hackers
from making changes in equipment configurations (Wachter, Mittelstadt & Floridi, 2017). For
restricting mobile device usage, Amazon can take certain steps like
 Local up of the server room
 Setting up Surveillance
 Pointing to the fact that vulnerable devices are completely locked up in the room.
 Making use of rack-mount servers
 Pack up of important backups
 Disabling the drivers
 Giving protection to the printers.
Logical Protection Mechanism
Logical Access control is the method by which the firm can implement security
constraints over data and digital platform. There is a list of logical access control measure found
at individual and team level for enhancing security policy at different levels (Schiffner et al.,
2018). Regulation of access provides the business of single user from having much control over
digital assets.
Amazon can make use of logical security measure on cyber-platform. With the passage of
time, authentication strategies are become much more advanced. The point can be concluded that

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traditional password authentication is becoming enough for the range of threats which the
business encounter (Demotes-Mainard et al., 2019). In this, token authentication is one of the
popular measures. Security Token can provide the user with a large number of changes in the
provided timeline.
Protecting heterogeneous information
Proper coordination of security policy in information is found to be a bit challenging as a
result of heterogeneity. Security Manager come up with a list of the tool at the time of disposal
for providing protecting to some vital information (Maguire, English & Draper, 2019). It is not
completely equipped for proper co-ordination of security policies in different interconnected
enterprises. Security in the state of heterogeneous environment is found like a patchwork for
overcoming conflict implemented in ad hoc way. Security managers for critical mission need to
assure the coherence of the policies in the global environment.
Protecting data in Motion
Amazon need to implement three basic step for providing data in motion like
Restricting cloud sharing: Some of the well-known cloud based application like
Dropbox and Google Drive can help individuals for bypassing the procurement department
(Danezis et al., 2015). One of the drawbacks of the application is that it does not meet the
corporate standard for protecting data and encryption.
Identification of critical assets: One of the most important component for securing data
in motion is all about managing risk by understanding frequency. Also, it focus on putting the

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firm on risk (Zhang, 2018). The most important aspect of data security lifecycle is all about the
classification of department, data, and people.
Implementing a security framework for the provided data: Employees of Amazon are
merely engaged in putting their data at risk, which is due to lack of IT policies (Wachter,
Mittelstadt & Floridi, 2017). The most suitable way is all about securing the data in motion and
moving to a multi-layer plan.
Data Protection Models
There are a list of data protection model like
Comprehensive model: It is completely based on omnibus approach that gives an
overarching protection to personal data. It comes up with data protection authority for
monitoring and enforcement of application in regulation (Schiffner et al., 2018). This model
combines both self-regulation and legislation so that it can provide support to regulation. This
mean that both government and industry aim to share responsibility for proper drafting and
putting regulations.
Sectoral Model: It is completely based on the idea that it can provide data protection or
even data privacy rules for some of the issues (Demotes-Mainard et al., 2019). This is completely
based on features of some industry like finance, health, and credit for data collection.
Self-regulatory model: It is a binding system that helps the organization to feel
compelled and comply with all the guidelines. The model is completely administered and
monitored by most of the non-governmental body which highlights category of organization.

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Making use of a device-centric or data-centric model
Data Centric Model: Data-centric refers to the complete architecture where data is stored
in primary and permanent asset. Here the application follow come and go model. In this
architecture, data model focus on implementing the given application (Maguire, English &
Draper, 2019). Each of the application system comes up with its own data model, and the code is
completely tied with the data model.
Device Centric architecture: Under this particular design, the device aims to maintain
both downlink and uplink connection (Danezis et al., 2015). This will help in carrying proper
control and data traffic with the base station cell.
For this case study, data-centric model will be suitable one because of
1. There is no misunderstanding of data
2. It is a simplified system
3. There will be no data owner outside Amazon.

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Fig 1: Data-Centric Model
(Source: Created By Author)
Encryption
Amazon needs to implement data encryption technology for providing protection to
personal information and customer data. Encryption is the basic process for making changes in
electronic details (Zhang, 2018). It merely allows the user to make changes in the information
back to original state so that it is usable and readable.

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References
Danezis, G., Domingo-Ferrer, J., Hansen, M., Hoepman, J. H., Metayer, D. L., Tirtea, R., &
Schiffner, S. (2015). Privacy and data protection by design-from policy to
engineering. arXiv preprint arXiv:1501.03726.
Demotes-Mainard, J., Cornu, C., Guerin, A., Bertoye, P. H., Boidin, R., Bureau, S., ... &
Duchossoy, L. (2019). How the new European data protection regulation affects clinical
research and recommendations?. Therapie, 74(1), 31-42.
Maguire, J., English, R., & Draper, S. (2019, January). Data protection and privacy regulations
as an inter-active-constructive practice. In Proceedings of the 3rd Conference on
Computing Education Practice (p. 9). ACM.
Schiffner, S., Berendt, B., Siil, T., Degeling, M., Riemann, R., Schaub, F., ... & Polonetsky, J.
(2018, June). Towards a roadmap for privacy technologies and the General Data
Protection Regulation: A transatlantic initiative. In Annual Privacy Forum (pp. 24-42).
Springer, Cham.
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated
decision-making does not exist in the general data protection regulation. International
Data Privacy Law, 7(2), 76-99.
Zhang, D. (2018, October). Big data security and privacy protection. In 8th International
Conference on Management and Computer Science (ICMCS 2018). Atlantis Press.